Best Data Science Course : The Complete Guide for Beginners

Data Science course is a booming domain with a high number of job openings. The good news is that irrespective of one's educational background and expertise, it is possible to pursue a career in Data Science with specialized training programs offered. Data Science requires knowledge of statistics, mathematics, and computer science. When coming to coding and programming, Data Science needs a basic understanding. Data Scientist has been in the top five career choices among the Millenials. Its relevance and demand are set to grow in the future as data-driven decisions are more profitable than viable. A senior Data Scientist with an experience of 10 years is offered RS 20-30 LPA while a junior Data Scientist with 5 years of experience is offered Rs 12-18 LPA. A fresher with minimum experience can land a Data Scientist job with Rs 8-12 LPA. There is a high influx of aspirants from various backgrounds, who wish to become Data Scientists, and this course is in high demand across organizations and industries. Studying Data Science promises a bright and lucrative career for everyone interested

So how do we learn? That is the question that we ask or try to find answers about.

• Data Science Training near me

  Also, check this Data Science Institute in Bangalore to start a career in Data Science.

  Since 2012, professional training organizations or EdTech startups have been engaged in the delivery of Data Science programs in online and offline mode. Due to COVID 2019, primarily all programs have been delivered in online mode for the time being which is taught by universities across the globe. The majority of programs that are offered are master’s in Data Science. A few undergraduate programs are also available. However, the masters program is what undergraduates can go for in. Aspirants can choose from either of the two options to learn Data Science from. However, the university route demands higher time and financial investment. A lot of aspirants prefer to go to professional training and certification institutes, which also have credible universities and professional organizations accreditations. They are much quicker, cost-effective/cheaper, and help in a fast turnaround in an aspirant’s learning journey with visible and realizable impact. So, depending on the time and finance constraints, one can take an informed decision.

• What is the Method to Study Data Science Training Program

  There is no dearth of Data Science training syllabus or resources. Education has been democratized to the extent that all knowledge is freely and conveniently available. Aspirants may want to learn the fundamentals on their own. It is a good idea though not sustainable. An instructor always makes a meaningful impact on the aspirant's learning journey. The concepts offered to get a bit technical and intense if one has to deep dive into it. Hence, to ease off the burden of overwhelming oneself with a waterfall of knowledge, hands-on experienced and practical instructors are needed. Also, the instructors are able to help navigate the aspirants in their quest for acquiring knowledge in a streamlined manner. Any good and experienced instructor will start the program by introducing the project management methodology of CRISP-DM to manage the project. More details about CRISP-DM methodology can be found out here ( link). The methodology lists out all the steps in a sequential manner to carry out a project. The concepts are learned from scratch. CRISP DM lists out the over methodology with 6 important steps as detailed below:

  

  • Understanding the Business Problem
  • Data Collection
  • Data Preparation
  • Data Mining
  • Model Evaluation
  • Model Deployment

• Data Science Course Requirements

  Can I build a good career in this domain? How can I quickly learn the fundamentals to get a Data Science job? Hey, but what is the eligibility for the course? Am I even eligible to under-go this coaching?”. Well, these are a few questions that cross an aspirant’s mind while contemplating moving ahead. This is a very good measure to take an informed decision. Most of the aspirants jump into doing program basis factors such as the cheapest online Data Science degree or guaranteed jobs. Even before taking that plunge, one needs to at least look at course materials and evaluate carefully.

  It has been strongly marketed and promoted that anyone can get into this field. This is true but an aspirant needs to understand what it takes to become a Data Scientist. Many times aspirants have their version of eligibility in mind due to fancy-sounding terms such as technology, algorithm, Machine Learning, etc. that deters them from making a fair evaluation. Rather than getting bogged down by jargon, one needs to look deeper and understand better.

  Looking forward to becoming a Data Scientist? Check out the Data Science Course and get certified today.

  The best way of evaluating one’s eligibility is to first list down one’s objective. Why does one want to become a Data Scientist or even fancy the idea? With the answer to that question in place, one can then look for broader areas of coverage to learn the fundamentals. The coverage aspect could consider factors such as time availability, the investment required, educational background, professional work experience, and course work. Let us take each of these factors and evaluate them.

  

  • Time: One can get into this field given that one is determined to commit and dedicate time every day for learning the fundamentals. Having said that, one also needs to decide whether one should do a full-time program through a university or one can go for short professional coaching. Time is of the essence and crucial for one’s success. One can succeed through any mode depending on their convenience. However, in the past success stories have majorly emerged through professional training institutions as universities across the globe have just started offering the Data Science syllabus.
  • Investment: We are a price-sensitive market and anything that sounds like a discount or cheapest draws our attention. A Data Science aspirant should not fall for such gimmicks. One needs to ensure the quality that one derives from the course curriculum. Hence, one can go for short term or long-term courses depending on investment available that could be diverted. Generally, the professional training institutes turn out to be extremely cost-effective, however, one needs to choose very cautiously.
  • Educational Background: There is an interesting trend that has emerged. There is more and more demand for talent from Social science and liberal arts backgrounds other than Applied Science and Engineering. So right from Liberal arts to Engineering sciences, there is a place for everyone.
  • Professional Work Experience: Previous work experience is highly valued in the field of Data Science. It is treated as domain expertise. Even an NGO activist can become a Data Scientist because he or she has a very good understanding of the social sector and data generated in the domain. So, when we develop AI solutions to curb human trafficking, such aspirants bring deep insights due to their domain expertise and add a lot of value because they also understand the modus operandi of such unlawful activities. This is just one scenario. Aspirants from a Fine Arts background have the power to disrupt the way visualization can be done using data. They can make it more engaging, and easy to interpret for making critical decisions. Also, people from technology and other sectors bring in their set of expertise to design new data-driven solutions.
  • Course work: Data Science is at the cross-section of Statistics, Mathematics, business, and Computer Science. Many aspirants freeze at the idea of Statistics, Mathematics, and coding. But not to worry. The level of concepts covered under Statistics and Mathematics is very basic and fundamental. Even aspirants from Social science and Liberal arts background can easily pick up the concepts. As far as coding is concerned, it is done using simple English language in open source tools such as R and Python. These are very user-friendly tools and extremely easy to learn. All the concepts taught work on these fundamental pillars.

  Mastering in Data Science tools and techniques will transform you into a professional Data Scientist. Let’s study the valuable insights to know what it takes to become a Successful Data Scientist.

• How to Become a Successful Data Scientist Training Online

  Data Science careers picked up steam in 2012 when Thomas Davenport claimed in a Harvard Business review that a Data Scientist will become one of the most fundamental pillars of any industry. Since then there has been no looking back for this career track. It has been reported that data scientists with 5 years of work experience are earning upwards of $200,000/- per annum in the USA. Across the globe, organizations are struggling to find the right talent. The demand for Data Scientists has skyrocketed.

  The demand has generated a frenzy in the market. Whether it is a fresher or someone with work experience, everybody is trying to get a share of this sunrise sector. Majority scholars and professionals irrespective of their backgrounds are upskilling themselves to learn the this course. The frenzy created in the market has made us believe that anyone can become a Master of Data Science. And this is true!! There is no dearth of Data Science learners. That has given a huge push to educational and affiliate businesses.

  In the past couple of years, the market has flooded with Data Science syllabus, resources, and Data Science books. Everything about data science paints a strong career story. However, learners and aspirants need to take a step back and first evaluate their educational background and professional experience. The background of aspirants can be split into two streams: technical and non-technical. This is important because an aspirant needs to work on strengths and core competence to build a career as a Data Scientist. The universe of this course is huge, deep, and ever-expanding.

  The more one dives into, it becomes intense and very technical to the extent of becoming engineering oriented. Hence, one can first decide and design the learning track to either gain expertise in the technical or non-technical stream. People from liberal arts or business backgrounds can look at gaining expertise in solving business challenges which could be very statistically inclined and people with a technical background such as Engineers can build expertise into more technology-oriented challenges.

  Once it is clear which stream to pursue, then the aspirant needs to critically evaluate time availability that can be committed for education. Many professional organizations and universities are offering programs in Data Science. The range of these courses is very wide. One can get options from fundamental courses to specialization tracks. The program duration ranges from 4 months to 24 months. Generally, university programs are for 14 months to 24 months. In this fast ever-evolving domain, majority aspirants opt for short duration courses, which gets them into the mainstream job market quickly. EdTech (professional training institutes) companies have contributed largely to the success of learners to date with their career aspirations. Also, the investment required to do the program is extremely cost-effective if pursued by a professional entity. Additionally, the faculty in such organizations are practical Data Scientists who have years of real-life work experience and make real value add to the learning experience.

  The Data Science market across the globe is slowly but gradually maturing up. The skill sets required to get a job as a Data Scientist has increased. Now organizations expect Data Scientists not only to have expertise in performing statistical analysis or building Machine learning models but also have experience in scaling the models, databases, cloud deployment, and automation. Hence, it is extremely critical to choose a program that addresses all the requirements. One can explore the job role requirements by exploring aggregated online job platforms. The job descriptions give first-hand insight into what employers expect from a Data Scientist today. Many times organizations may demand skills that would not be required for the job role; however, they try to build capacity for the future. To meet such expectations, an aspirant needs to choose a curated program that addresses and meets the market demand practically.

  Earn yourself a promising career in data science by enrolling in the Data Science Classes in Pune offered by 360DigiTMG.

  Organizations expect Data Scientists to have hands-on experience from basic statistics to advance Artificial Intelligence. Data Science learner needs to ensure that the course covers the below aspects:

  • Basic and inferential Statistics
  • Mathematical concepts (linear algebra and multivariate calculus)
  • Classical Machine Learning (supervised and unsupervised)
  • Artificial Intelligence (Deep Learning that involves neural networks)
  • Visualization and Reporting (using Tableau, QlikView, etc.)
  • Big Data Storage (using Hadoop, Hive, etc.)
  • Databases (Relational: SQL, MongoDB, Non-Relational: NoSQL, etc.)
  • Machine Learning on Cloud (AWS, Azure etc.)
  • Analytical tools (R, Python, Apache Spark, SAS, etc.)
  • Real-Time Data Handling (Apache Kafka, Amazon Kinesis, Flink, etc.)
  • Data Science Project Management Method (CRISP-DM)
  • Version control (Git and GitHub)

  A Data Science training covering all these aspects is guaranteed to prepare a fundamentally strong Data Scientist. To solidify one’s learning, the need is to understand the concepts of Statistics, Mathematics, and Machine Learning algorithms in depth along with intense hands-on practice through various assignments attached to every topic. The more one practices the better one becomes. An aspiring Data Scientist should ensure that the Data Science program they enroll in allows them to work on live real-life projects that can further strengthen their profile and learning. One needs to work on multiple such projects to build a great portfolio to showcase. Please do not make the mistake of building up your portfolio with tried and tested case studies such as MNIST, Titanic, or Iris. Try to get more hands-on experience by working on live hackathons hosted by platforms such as Kaggle. Ensure to have your profile listed on Kaggle and GitHub. It is extremely important to have a professional networking profile on LinkedIn showcasing your expertise. Recruiters prefer to reach out to prospective Data Scientist employees through professional networks.

  A journey to becoming a Data Scientist is very intense yet extremely rewarding. Data Scientists perform painstaking tasks for organizations and hence they are hugely valued and respected in the industry. The fat pay cheques are a gesture of gratitude that the Data Scientists command. To sum it up, it is a great time to be a Data Scientist, however, one needs to be strong-willed and dedicated to giving oneself time to learn and practice the above-mentioned concepts and tools. That is the only road to becoming a great Data Scientist.

• Data Science Course Modules

  This course espouses the CRISP-DM Project Management Methodology. A primer on statistics, DATA VISUALIZATION, plots, and Inferential Statistics, and Probability Distribution is contained in the premier modules of the course. The subsequent modules deal with Exploratory Data Analysis, Hypothesis Testing, and Data Mining Supervised Learning-enabled with Linear Regression and OLS. The following modules focus on the various regression models. We learn to enable Predictive Modeling with Multiple Linear Regression. The merits of Lasso and Ridge Regression, Logistic Regression, Multinomial Regression, and Advanced Regression For Count Data are explored. Data Mining Unsupervised Learning is the fulcrum of the next three modules. The various approaches used to enable the same like Clustering, Dimension Reduction, and Association Rules are elaborated in-depth with appropriate algorithms. The workings of Recommendation Engines and the key concepts of Network Analytics are also detailed.
  
  This Data Science Courses in India lends focus to Machine Learning algorithms like k-NN Classifier, Decision Tree and Random Forest, Ensemble Techniques- Bagging and Boosting, AdaBoost, Extreme Gradient Boosting, and Naive Bayes algorithm. Text Mining and Natural Language Processing also feature in the course curriculum. The building blocks of Neural Networks -ANN and Deep Learning Black Box Techniques like CNN, RNN, and SVM are also described in great detail. The concluding modules include model-driven and data-driven algorithm development for forecasting and Time Series Analysis. This is the most comprehensive course from the best data science training institute in India.

  1. CRISP – DM - Project Management Methodology

  Learn about insights on how data is assisting organizations to make informed data-driven decisions. Data is treated as the new oil for all the industries and sectors which keep organizations ahead in the competition. Learn the application of Big Data Analytics in real-time, you will understand the need for analytics with a use case. Also, learn about the best project management methodology for Data Mining - CRISP-DM at a high level.

   

  • All About 360DigiTMG & Innodatatics Inc., USA
  • Dos and Don'ts as a participant
  • Introduction to Big Data Analytics
  • Data and its uses – a case study (Grocery store)
  • Interactive marketing using data & IoT – A case study
  • Course outline, road map, and takeaways from the course
  • Stages of Analytics - Descriptive, Predictive, Prescriptive, etc.
  • Cross-Industry Standard Process for Data Mining

  2. Exploratory Data Analytics (EDA) / Descriptive Analytics

  Data Science project management methodology, CRISP-DM will be explained in this module in finer detail. Learn about Data Collection, Data Cleansing, Data Preparation, Data Munging, Data Wrapping, etc. Learn about the preliminary steps taken to churn the data, known as exploratory data analysis. In this module, you also are introduced to statistical calculations which are used to derive information from data. We will begin to understand how to perform a descriptive analysis.

   

  • Machine Learning project management methodology
  • Data Collection - Surveys and Design of Experiments
  • Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification and application
  • Further classification of data in terms of Nominal, Ordinal, Interval & Ratio types
  • Balanced versus Imbalanced datasets
  • Cross Sectional versus Time Series vs Panel / Longitudinal Data
  • Batch Processing vs Real Time Processing
  • Structured versus Unstructured vs Semi-Structured Data
  • Big vs Not-Big Data
  • Data Cleaning / Preparation - Outlier Analysis, Missing Values Imputation Techniques, Transformations, Normalization / Standardization, Discretization
  • Sampling techniques for handling Balanced vs. Imbalanced Datasets
  • What is the Sampling Funnel and its application and its components?
    • Population
    • Sampling frame
    • Simple random sampling
    • Sample
  • Measures of Central Tendency & Dispersion
    • Population
    • Mean/Average, Median, Mode
    • Variance, Standard Deviation, Range

  3. Statistical Data Business Intelligence and Data Visualization

  Learn about various statistical calculations used to capture business moments for enabling decision makers to make data driven decisions. You will learn about the distribution of the data and its shape using these calculations. Understand to intercept information by representing data by visuals. Also learn about Univariate analysis, Bivariate analysis and Multivariate analysis.

   

  • Measure of Skewness
  • Measure of Kurtosis
  • Spread of the Data
  • Various graphical techniques to understand data
    • Bar Plot
    • Histogram
    • Boxplot
    • Scatter Plot

  4. Plots & Inferential Statistics

  Data Visualization helps understand the patterns or anomalies in the data easily and learn about various graphical representations in this module. Understand the terms univariate and bivariate and the plots used to analyze in 2D dimensions. Understand how to derive conclusions on business problems using calculations performed on sample data. You will learn the concepts to deal with the variations that arise while analyzing different samples for the same population using the central limit theorem.

   

  • Line Chart
  • Pair Plot
  • Sample Statistics
  • Population Parameters
  • Inferential Statistics

  5. Probability Distributions (Continuous & Discrete)

  Want to learn more about data science? Enroll in the Best Data Science courses in Chennai to do so.

  In this tutorial you will learn in detail about continuous probability distribution. Understand the properties of a continuous random variable and its distribution under normal conditions. To identify the properties of a continuous random variable, statisticians have defined a variable as a standard, learning the properties of the standard variable and its distribution. You will learn to check if a continuous random variable is following normal distribution using a normal Q-Q plot. Learn the science behind the estimation of value for a population using sample data.

   

  • Random Variable and its definition
  • Probability & Probability Distribution
    • Continuous Probability Distribution / Probability Density Function
    • Discrete Probability Distribution / Probability Mass Function
  • Normal Distribution
  • Standard Normal Distribution / Z distribution
  • Z scores and the Z table
  • QQ Plot / Quantile - Quantile plot
  • Sampling Variation
  • Central Limit Theorem
  • Sample size calculator
  • Confidence interval - concept
  • Confidence interval with sigma
  • T-distribution / Student's-t distribution
  • Confidence interval
    • Population parameter with Standard deviation known
    • Population parameter with Standard deviation not known
  • A complete recap of Statistics

  6. Hypothesis Testing - The ‘4’ Must Know Hypothesis Tests

  Learn to frame business statements by making assumptions. Understand how to perform testing of these assumptions to make decisions for business problems. Learn about different types of Hypothesis testing and its statistics. You will learn the different conditions of the Hypothesis table, namely Null Hypothesis, Alternative hypothesis, Type I error and Type II error. The prerequisites for conducting a Hypothesis test, interpretation of the results will be discussed in this module.

   

  • Formulating a Hypothesis
  • Choosing Null and Alternative Hypothesis
  • Type I or Alpha Error and Type II or Beta Error
  • Confidence Level, Significance Level, Power of Test
  • Comparative study of sample proportions using Hypothesis testing
  • 2 Sample t-test
  • ANOVA
  • 2 Proportion test
  • Chi-Square test

  7. Data Mining Supervised Learning – Linear Regression, OLS

  Data Mining supervised learning is all about making predictions for an unknown dependent variable using mathematical equations explaining the relationship with independent variables. Revisit the school math with the equation of a straight line. Learn about the components of Linear Regression with the equation of the regression line. Get introduced to Linear Regression analysis with a use case for prediction of a continuous dependent variable. Understand about ordinary least squares technique.

   

  • Scatter diagram
    • Correlation analysis
    • Correlation coefficient
  • Ordinary least squares
  • Principles of regression
  • Simple Linear Regression
  • Exponential Regression, Logarithmic Regression, Quadratic or Polynomial Regression
  • Confidence Interval versus Prediction Interval
  • Heteroscedasticity / Equal Variance

  8. Predictive Modelling – Multiple Linear Regression

  In the continuation to Regression analysis study you will learn how to deal with multiple independent variables affecting the dependent variable. Learn about the conditions and assumptions to perform linear regression analysis and the workarounds used to follow the conditions. Understand the steps required to perform the evaluation of the model and to improvise the prediction accuracies. You will be introduced to concepts of variance and bias.

   

  • LINE assumption
    • Linearity
    • Independence
    • Normality
    • Equal Variance / Homoscedasticity
  • Collinearity (Variance Inflation Factor)
  • Multiple Linear Regression
  • Model Quality metrics
  • Deletion Diagnostics

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  9. Lasso and Ridge Regressions

  Learn about overfitting and underfitting conditions for prediction models developed. We need to strike the right balance between overfitting and underfitting, learn about regularization techniques L1 norm and L2 norm used to reduce these abnormal conditions. The regression techniques Lasso and Ridge techniques are discussed in this module .

   

  • Understanding Overfitting (Variance) vs. Underfitting (Bias)
  • Generalization error and Regularization techniques
  • Different Error functions or Loss functions or Cost functions
  • Lasso Regression
  • Ridge Regression

  10. Logistic Regression – Binary Value Prediction, MLE

  You have learnt about predicting a continuous dependent variable. As part of this module, you will continue to learn Regression techniques applied to predict attribute Data. Learn about the principles of the logistic regression model, understand the sigmoid curve, the usage of cutoff value to interpret the probable outcome of the logistic regression model. Learn about the confusion matrix and its parameters to evaluate the outcome of the prediction model. Also, learn about maximum likelihood estimation.

   

  • Principles of Logistic regression
  • Types of Logistic regression
  • Assumption & Steps in Logistic regression
  • Analysis of Simple logistic regression results
  • Multiple Logistic regression
  • Confusion matrix
    • False Positive, False Negative
    • True Positive, True Negative
    • Sensitivity, Recall, Specificity, F1
  • Receiver operating characteristics curve (ROC curve)
  • Precision Recall (P-R) curve
  • Lift charts and Gain charts

  11. Multinomial Regression

  Extension to logistic regression We have a multinomial regression technique used to predict a multiple categorical outcome. Understand the concept of multi logit equations, baseline and making classifications using probability outcomes. Learn about handling multiple categories in output variables including nominal as well as ordinal data.

   

  • Logit and Log-Likelihood
  • Category Baselining
  • Modeling Nominal categorical data
  • Handling Ordinal Categorical Data
  • Interpreting the results of coefficient values

  12. Advanced Regression for Count Data

  As part of this module you learn further different regression techniques used for predicting discrete data. These regression techniques are used to analyze the numeric data known as count data. Based on the discrete probability distributions namely Poisson, negative binomial distribution the regression models try to fit the data to these distributions. Alternatively, when excessive zeros exist in the dependent variable, zero-inflated models are preferred, you will learn the types of zero-inflated models used to fit excessive zeros data.

   

  • Poisson Regression
  • Poisson Regression with Offset
  • Negative Binomial Regression
  • Treatment of data with Excessive Zeros
    • Zero-inflated Poisson
    • Zero-inflated Negative Binomial
    • Hurdle Model

  13. Machine Learning - k -NN Classifier

  k Nearest Neighbor algorithm is distance based machine learning algorithm. Learn to classify the dependent variable using the appropriate k value. The k-NN classifier also known as lazy learner is a very popular algorithm and one of the easiest for application.

   

  • Deciding the K value
  • Thumb rule in choosing the K value
  • Building a KNN model by splitting the data
  • Checking for Underfitting and Overfitting in KNN
  • Generalization and Regulation Techniques to avoid overfitting in KNN

  14. Decision Tree & Random Forest

  Decision Tree & Random forest are some of the most powerful classifier algorithms based on classification rules. In this tutorial, you will learn about deriving the rules for classifying the dependent variable by constructing the best tree using statistical measures to capture the information from each of the attributes. Random forest is an ensemble technique constructed using multiple Decision trees and the final outcome is drawn from the aggregating the results obtained from these combinations of trees.

   

  • Elements of classification tree - Root node, Child Node, Leaf Node, etc.
  • Greedy algorithm
  • Measure of Entropy
  • Attribute selection using Information gain
  • Ensemble techniques - Stacking, Boosting and Bagging
  • Decision Tree C5.0 and understanding various arguments
  • Checking for Underfitting and Overfitting in Decision Tree
  • Generalization and Regulation Techniques to avoid overfitting in Decision Tree
  • Random Forest and understanding various arguments
  • Checking for Underfitting and Overfitting in Random Forest
  • Generalization and Regulation Techniques to avoid overfitting in Random Forest

  15. Ensemble Techniques - Bagging and Boosting

  Learn about improving reliability and accuracy of decision tree models using ensemble techniques. Bagging and Boosting are the go to techniques in ensemble techniques. The parallel and sequential approaches taken in Bagging and Boosting methods are discussed in this module.

   

  • Overfitting
  • Underfitting
  • Pruning
  • Boosting
  • Bagging or Bootstrap aggregating

  16. AdaBoost & Extreme Gradient Boosting

  The Boosting algorithms AdaBoost and Extreme Gradient Boosting are discussed as part of this continuation module You will also learn about stacking methods. Learn about these algorithms which are providing unprecedented accuracy and helping many aspiring data scientists win the first place in various competitions such as Kaggle, CrowdAnalytix, etc.

  • AdaBoost / Adaptive Boosting Algorithm
  • Checking for Underfitting and Overfitting in AdaBoost
  • Generalization and Regulation Techniques to avoid overfitting in AdaBoost
  • Gradient Boosting Algorithm<
  • Checking for Underfitting and Overfitting in Gradient Boosting
  • Generalization and Regulation Techniques to avoid overfitting in Gradient Boosting
  • Extreme Gradient Boosting (XGB) Algorithm
  • Checking for Underfitting and Overfitting in XGB
  • Generalization and Regulation Techniques to avoid overfitting in XGB

  17. Text Mining and Natural Language Processing (NLP)

  Learn to analyse the unstructured textual data to derive meaningful insights. Understand the language quirks to perform data cleansing, extract features using a bag of words and construct the key-value pair matrix called DTM. Learn to understand the sentiment of customers from their feedback to take appropriate actions. Advanced concepts of text mining will also be discussed which help to interpret the context of the raw text data. Topic models using LDA algorithm, emotion mining using lexicons are discussed as part of NLP module.

   

  • Sources of data
  • Bag of words
  • Pre-processing, corpus Document Term Matrix (DTM) & TDM
  • Word Clouds
  • Corpus level word clouds
    • Sentiment Analysis
    • Positive Word clouds
    • Negative word clouds
    • Unigram, Bigram, Trigram
  • Semantic network
  • Clustering
  • Extract user reviews of the product/services from Amazon, Snapdeal and trip advisor
  • Install Libraries from Shell
  • Extraction and text analytics in Python
  • LDA / Latent Dirichlet Allocation
  • Topic Modelling
  • Sentiment Extraction
  • Lexicons & Emotion Mining

  18. Machine Learning Classifier Technique - Naive Bayes

  Revise Bayes theorem to develop a classification technique for Machine learning. In this tutorial you will learn about joint probability and its applications. Learn how to predict whether an incoming email is a spam or a ham email. Learn about Bayesian probability and the applications in solving complex business problems.

   

  • Probability – Recap
  • Bayes Rule
  • Naïve Bayes Classifier
  • Text Classification using Naive Bayes
  • Checking for Underfitting and Overfitting in Naive Bayes
  • Generalization and Regulation Techniques to avoid overfitting in Naive Bayes

  19. Introduction to Perceptron and Multilayer Perceptron

  Perceptron algorithm is defined based on a biological brain model. You will talk about the parameters used in the perceptron algorithm which is the foundation of developing much complex neural network models for AI applications. Understand the application of perceptron algorithms to classify binary data in a linearly separable scenario.

   

  • Neurons of a Biological Brain
  • Artificial Neuron
  • Perceptron
  • Perceptron Algorithm
  • Use case to classify a linearly separable data
  • Multilayer Perceptron to handle non-linear data

  20. Building Blocks of Neural Network - ANN

  Neural Network is a black box technique used for deep learning models. Learn the logic of training and weights calculations using various parameters and their tuning. Understand the activation function and integration functions used in developing a neural network.

   

  • Integration functions
  • Activation functions
  • Weights
  • Bias
  • Learning Rate (eta) - Shrinking Learning Rate, Decay Parameters
  • Error functions - Entropy, Binary Cross Entropy, Categorical Cross Entropy, KL Divergence, etc.

  21. Deep Learning Primer

  • Artificial Neural Networks
  • ANN Structure
  • Error Surface
  • Gradient Descent Algorithm
  • Backward Propagation
  • Network Topology
  • Principles of Gradient Descent (Manual Calculation)
  • Learning Rate (eta)
  • Batch Gradient Descent
  • Stochastic Gradient Descent
  • Minibatch Stochastic Gradient Descent
  • Optimization Methods: Adagrad, Adadelta, RMSprop, Adam
  • Convolution Neural Network (CNN)
    • ImageNet Challenge – Winning Architectures
    • Parameter Explosion with MLPs
    • Convolution Networks
  • Recurrent Neural Network
    • Language Models
    • Traditional Language Model
    • Disadvantages of MLP
    • Back Propagation Through Time
    • Long Short-Term Memory (LSTM)
    • Gated Recurrent Network (GRU)

  22. Kernel Method - SVM

  • Support Vector Machines / Large-Margin / Max-Margin Classifier
  • Hyperplanes
  • Best Fit "boundary"
  • Linear Support Vector Machine using Maximum Margin
  • SVM for Noisy Data
  • Non- Linear Space Classification
  • Non-Linear Kernel Tricks
    • Linear Kernel
    • Polynomial
    • Sigmoid
    • Gaussian RBF
  • SVM for Multi-Class Classification
    • One vs. All
    • One vs. One
  • Directed Acyclic Graph (DAG) SVM

  23. Data Mining Unsupervised Learning – Clustering

  Data mining unsupervised techniques are used as EDA techniques to derive insights from the business data. In this first module of unsupervised learning, get introduced to clustering algorithms. Learn about different approaches for data segregation to create homogeneous groups of data. Hierarchical clustering, K means clustering are most commonly used clustering algorithms. Understand the different mathematical approaches to perform data segregation. Also learn about variations in K-means clustering like K-medoids, K-mode techniques, learn to handle large data sets using CLARA technique.

   

  • • Hierarchical • Supervised vs Unsupervised learning • Data Mining Process • Hierarchical Clustering / Agglomerative Clustering • Dendrogram • Measure of distance
    • Numeric
      • Euclidean, Manhattan, Mahalanobis
    • Categorical
      • Binary Euclidean
      • Simple Matching Coefficient
      • Jaquard's Coefficient
    • Mixed
      • Gower's General Dissimilarity Coefficient
    • Types of Linkages
      • Single Linkage / Nearest Neighbour
      • Complete Linkage / Farthest Neighbour
      • Average Linkage
      • Centroid Linkage
    • K-Means Clustering
      • Measurement metrics of clustering
        • Within the Sum of Squares
        • Between the Sum of Squares
        • Total Sum of Squares
      • Choosing the ideal K value using Scree Plot / Elbow Curve
      • Other Clustering Techniques
        • K-Medians
        • K-Medoids
        • K-Modes
        • Clustering Large Application (CLARA)
        • Partitioning Around Medoids (PAM)
        • Density-based spatial clustering of applications with noise (DBSCAN)

  24. Data Mining Unsupervised Learning - Dimension Reduction (PCA)

  Dimension Reduction (PCA) / Factor Analysis Description: Learn to handle high dimensional data. The performance will be hit when the data has a high number of dimensions and machine learning techniques training becomes very complex, as part of this module you will learn to apply data reduction techniques without any variable deletion. Learn the advantages of dimensional reduction techniques. Also, learn about yet another technique called Factor Analysis.

   

  • Why Dimension Reduction
  • Advantages of PCA
  • Calculation of PCA weights
  • 2D Visualization using Principal components
  • Basics of Matrix Algebra
  • Factor Analysis

  25. Data Mining Unsupervised Learning - Association Rules

  Learn to measure the relationship between entities. Bundle offers are defined based on this measure of dependency between products. Understand the metrics Support, Confidence and Lift used to define the rules with the help of Apriori algorithm. Learn pros and cons of each of the metrics used in Association rules.

   

  • What is Market Basket / Affinity Analysis
  • Measure of Association
    • Support
    • Confidence
    • Lift Ratio
  • Apriori Algorithm
  • Sequential Pattern Mining

  26. Recommendation Engine

  Personalized recommendations made in e-commerce are based on all the previous transactions made. Learn the science of making these recommendations using measuring similarity between customers. The various methods applied for collaborative filtering, their pros and cons, SVD method used for recommendations of movies by Netflix will be discussed as part of this module.

   

  • User-based Collaborative Filtering
  • A measure of distance/similarity between users
  • Driver for Recommendation
  • Computation Reduction Techniques
  • Search based methods/Item to Item Collaborative Filtering
  • SVD in recommendation
  • The vulnerability of recommendation systems

  27. Network Analytics

  Study of a network with quantifiable values is known as network analytics. The vertex and edge are the node and connection of a network, learn about the statistics used to calculate the value of each node in the network. You will also learn about the google page ranking algorithm as part of this module.

   

  • Definition of a network (the LinkedIn analogy)
  • The measure of Node strength in a Network
    • Degree centrality
    • Closeness centrality
    • Eigenvector centrality
    • Adjacency matrix
    • Betweenness centrality
    • Cluster coefficient
  • Introduction to Google page ranking

  28. Auto Machine Learning (Auto ML)

  • AutoML Methods
  • AutoML Systems
  • AutoML on Cloud - AWS
    • Amazon SageMaker
    • Sagaemaker Notebook Instance for Model Development, Training and
    • Deployment
    • XG Boost Classification Model
    • Hyperparameter tuning jobs
  • AutoML on Cloud - Azure
    • Workspace
    • Environment
    • Compute Instance
    • Automatic Featurization
    • AutoML and ONNX
  • AutoML on Cloud - GCP
    • AutoML Natural Language Performing Document Classification
    • Performing Sentiment Analysis using AutoML Natural Language API
    • Cloud ML Engine and Its Components
    • Training and Deploying Applications on Cloud ML Engine
    • Choosing Right Cloud ML Engine for Training Jobs

  29. Survival Analytics

  Kaplan Meier method and life tables are used to estimate the time before the event occurs. Survival analysis is about analyzing this duration or time before the event. Real-time applications of survival analysis in customer churn, medical sciences and other sectors is discussed as part of this module. Learn how survival analysis techniques can be used to understand the effect of the features on the event using Kaplan Meier survival plot.

   

  • Examples of Survival Analysis
  • Time to event
  • Censoring
  • Survival, Hazard, Cumulative Hazard Functions
  • Introduction to Parametric and non-parametric functions

  30. Forecasting/Time Series – Model-Driven Algorithms

  Time series analysis is performed on the data which is collected with respect to time. The response variable is affected by time. Understand the time series components, Level, Trend, Seasonality, Noise and methods to identify them in a time series data. The different forecasting methods available to handle the estimation of the response variable based on the condition of whether the past is equal to the future or not will be introduced in this module. In this first module of forecasting, you will learn the application of Model-based forecasting techniques.

   

  • Introduction to time series data
  • Steps to forecasting
  • Components to time series data
  • Scatter plot and Time Plot
  • Lag Plot
  • ACF - Auto-Correlation Function / Correlogram
  • Visualization principles
  • Naïve forecast methods
  • Errors in the forecast and it metrics - ME, MAD, MSE, RMSE, MPE, MAPE
  • Model-Based approaches
    • Linear Model
    • Exponential Model
    • Quadratic Model
    • Additive Seasonality
    • Multiplicative Seasonality
  • Model-Based approaches Continued
  • AR (Auto-Regressive) model for errors
  • Random walk

  31. Forecasting/Time Series - Data-Driven Algorithms

  In this continuation module of forecasting learn about data-driven forecasting techniques. Learn about ARMA and ARIMA models which combine model-based and data-driven techniques. Understand the smoothing techniques and variations of these techniques. Get introduced to the concept of de-trending and deseasonalize the data to make it stationary. You will learn about seasonal index calculations which are used for reseasonalize the result obtained by smoothing models.

   

  • ARMA (Auto-Regressive Moving Average), Order p and q
  • ARIMA (Auto-Regressive Integrated Moving Average), Order p, d, and q
  • A data-driven approach to forecasting
  • Smoothing techniques
    • Moving Average
    • Exponential Smoothing
    • Holt's / Double Exponential Smoothing
    • Winters / Holt-Winters
  • De-seasoning and de-trending
  • Econometric Models
  • Forecasting using Python
  • Forecasting using R

• Why is the world giving much emphasis on Data Science? How Best Data Science Certificate Programs Will Change the world?

  Data Science has significantly changed the world. From curbing human trafficking to early diagnosis of autism to fighting global warming to creating sustainable business impact through prudent decision making, Data Science has left its mark everywhere. And these are just a few examples from various domains. The benefits are ever-evolving. The importance has increased not only as a tool for the benefit of business but also for the greater good to society. So, let us try to understand the real purpose of why it is needed.

  The fundamental responsibility is to look at the data irrespective of the domain to drive patterns and insights out of it. The scope involves crystallizing and understanding the problem at hand and document the objective and constraint of the problem. Then the relevant data is collected from secondary or primary sources depending on availability. Generally, data collected is in a messy format and that is why data scientists are required to work around it. The Data Scientist will wrangle and pre-process the data to put it in a clean and structured format for analysis. Once the data is thoroughly scrutinized, the Data Scientist puts it to use for generating predictions that are used by businesses to devise a strategy for increasing the profits, by healthcare domain to detect and prevent health issues, by social scientists to understand human behavior, etc. In nutshell, make smarter, well informed, and well-evaluated decisions.

  There has been an outburst of data since social media opened up. Data is available in all forms such as audio, video, images, text, etc. Data is being generated in exabytes (1 exabyte = 100,00,00,000 gigabytes). This has given birth to the era of Big data and Artificial Intelligence. And the need to use the data has grown manifold. Be it business organizations, governments, intelligence agencies, educational institutions, hospitals, not for profit organizations, etc. Data has generated immense opportunities and possibilities. Interestingly, earlier statisticians used to analyze data and help organizations do better. But as the cost of storing data declined, storage capacity increased through cloud platforms, and computational power increased, multiple knowledge streams other than statistics were employed which gave birth to Data Science engineering methods.

  So, Data Science gained traction as a beautiful mix of statistics and computational power. Manually it is only possible for us to simulate a handful of scenarios around any problem but this infused power to simulate scenarios with innumerable dimensions. The scope is so wide that it is helping us grow and design a better future by bolstering innovation. It helps us build advanced and ergonomic products which are sustainable solutions for future generations. We do not discount the fact that there are other knowledge streams, which contribute to such developments. However, in the beginning, there is always data that needs to be evaluated whether it is structured or unstructured, qualitative or quantitative, etc.

  Jack Ma, founder of Chinese e-commerce giant Alibaba, in one of his interviews said that it will be extremely challenging to make machines learn empathy. Well, we agree!! However, there are organizations such as Quilt.AI that are building human empathy at scale through the machines using the power of Data Science and Anthropology. Recently, they conducted a study in the state of Rajasthan for young boys under the age of 18 to understand “facets of masculinity” and how do these facets form their behavior towards women. With the insights gathered using Data, they are launching a behavior change campaign to curb virtual eve-teasing and misogynistic conduct. Property tax collections have been made possible using sophisticated techniques that involve computer vision and Deep Learning algorithms.

  Data Science is an extremely powerful tool to solve complex problems by learning patterns in the data. Companies use it to enhance customer experience, better marketing strategies, increase efficiencies in their operations, etc. Governments employ for better governance and devising sustainable public policies. The HealthCare industry also uses Data to understand human health for helping people to manage their health in a better way. This has become the backbone of the world. And it for sure will enrich our lives and generations to come.

• Benefits of Data Science for Business

  The biggest benefactors of Data Science are businesses. Most of the groundbreaking innovations have been reported in the business domain. One of the primary reasons is that over the period the cost of storing and managing data has substantially reduced. Hence, the organizations can store the generated data and take benefit from it with the help of Data Scientist. The scope is very wide as the domain is very versatile. It can be easily integrated and implemented in any business scenario based on the availability of data. So, let us understand why Data Science is needed and how it can benefit a business.

  The components include defining the business problem, collecting the relevant data, cleaning and preprocessing the data, driving insights, building predictive models, and using the predictions to design business strategy to drive maximum mileage out of it. This methodology is simple and easily implemented. However, every step of the method has its challenges and takes on those challenges head-on. Every department of the business generates a lot of data and with the help of the Data Science process, it is put to best use to positively impact the top and bottom line. We can try to understand the value derived by looking at a few business departments.

  

  • Sales - It is one of the most important verticals of any business as it is a direct revenue generation hub. The sales staff daily rally in the market to sell the services or products of the business by connecting with new and existing set off clients. Data Science helps in good quality lead generation and lead conversion. Also, through sophisticated techniques such as Natural Language Processing, customer responses are automated for the questions that are raised by prospective clients at the pre-sales stage. This helps in the early onboarding of the clients. This means that the sales team can achieve more by addressing more customers thereby increasing the business volume.
  • Marketing – A business can be in the distribution of multiple products. In real-time, it is quite tedious to match the right products to personalized customer needs. With the help of Data Science, the marketing team can mine the historical data for patterns for consumer needs and identify cross-sell opportunities. This helps in addressing the needs of that segment of customers as well, who have not been engaged regularly by the business. This reduces customer churn, builds customer loyalty, and increases customer lifetime value for the business.
  • Human Resource – Workforce analytics is a product of Data Science. The human resource department of business benefits immensely with this. For ages, talent recruitment to manage employee benefits and compensation has been a tough and time-consuming activity. With workforce analytics, matching the right talent with the business requirement and identifying the right level of compensation has reduced inertia. Human Resource teams with the help of bots are able to declutter the data overload that is generated from applicant resumes for a job role. The bot accurately identifies and segregates the applicants with the right skill set. Also, with workforce analytics employee attribution can be controlled as it throws early warning signs by looking into employee behavior and conduct.
  • Operations – The back office of any business is the backbone of all activities critical for a service or product delivery. Many times a business can claim that it is extremely difficult to track the progress of work. Like in infrastructure projects such as Real estate, tracking daily developments is a real task and challenging to define. To solve this issue, Data Scientists place multiple sensors in the safety wear of the construction staff to get the real-time feed of data to track task completion daily. Using computer vision, Data Scientists help the operations team to maintain optimum quality in production lines across manufacturing and services.
  • Treasury – Cash management and investments are the sole responsibility of the Treasury department of a business. It also ensures that through treasury operations, the business is able to grow its bottom line. This is challenged due to the unclear use of cash by business and the unknown cost of banking. Using the treasury department is able to look into historical data of payments and receivables along with forecasted values to identify the real cash needed to various business departments. The treasurer employs Data methods to analyze the transactions against bank statements to identify the high-cost scenarios to control cash loss.
  • Security – Irrespective of the size of the business, it has to ensure the security of its premises and employees. It is responsible to curb any unauthorized access and secure employees from any instance of physical harm. Using computer vision and Deep Learning algorithm, tracking and self-operating devices can be deployed that use facial recognition to identify employees and related staff from outsiders to control access and also to track the physical location of employees on business premises to rescue in case of any unforeseen event.

  These are just a handful of uses of Data for business. If we look at the business from the perspective of supply chain wise or value chain wise, then we can additionally unearth innumerable benefits of Data Science for any business. What is Data Analytics? Is it similar or different from Data Science? These topics are discussed in detail below.

• Difference between Data Science and Analytics

  In the data-driven economy, it is imperative to have data skills. Everybody is rushing to hone data skills to be relevant for the future job market. However, more often than not people get stuck with the question of whether they should go for a Data Analytics or Data Science. To solve the state of quandary, let us first understand how to differentiate between the Data Analytics definition and the Data Science definition. Read an article on Future of Data Scientist.

  Both are reticular in nature. However, the approach and results are very different for the two. Data Analytics focuses on descriptive and diagnostic aspects to answer the questions that we are aware of. What happened and why did it happen? The advantage of analytics is that we can generate actionable insights by organizing, processing, and presenting the data in the best possible way. The insights generated from Data Analytics methods can be implemented to get quick improvements. Data Analytics uses statistical methods to explore and analyze data. It is only a precursor to Data Science.

  Data Science is a cross-section of Statistics and Machine Learning. A Data Scientist applies many different methods to provide answers for problems that could not have been conceived until now by parsing through massive data. The two advantages of Data Science are predictive and prescriptive analytics. The Data Scientist's primary agenda is to find the right questions to ask rather than giving pointed answers. The Data Scientist engages in exploring heterogeneous and disjoint data to find better ways of analyzing and predicting potential trends. The Data Scientist goes a step beyond to prescribe measures over the predictions generated for strategic planning that has a sustainable impact. This adds value to the significance of Data Science.

  

  Due to the inherent difference in the nature of Data Analytics and Data Science, there is a huge difference between the salaries commanded by Data Analysts and Data Scientists. The salaries range from upwards of INR 4.5 lakhs per annum. A fresher may get INR 4.5 lakhs to INR 12.5 lakhs per annum whereas for experienced career transitioners the salaries range from INR 25 lakhs to INR 30 lakhs per annum. The Data Analytics salaries for freshers range from INR 1.7 lakhs to INR 6.5 lakhs per annum and for experienced professionals, it ranges from INR 8.5 lakhs to INR 20 lakhs per annum. The valuation of the two streams stems from the complexity of tasks performed by a Data Analyst vis a vis a Data scientist. A Data Scientist covers all the critical tasks that a data analyst performs and goes much beyond that to add more value. However, salaries should not be a parameter for any aspirant to decide between the two streams of knowledge. One should choose to become either a Data Analyst or a Data Scientist by carefully evaluating one’s skill set and motivation.

  For further information and insights in this regard, we are available to assist and throw more light on how businesses can further benefit. Please refer here to contact us.