Certification Program in Data Science
- 130 Hours Classroom & Online Sessions
- 140 Hours Assignments & Real-Time Projects
- Complimentary Python and R Programming Beginners Course
"With hundreds of companies hiring for the role of Data Scientist, 12 million new jobs will be created in the field of Data Science by the year 2026. " - (Source). From the last four years, the Data Scientist job is ranked as number one in U.S by Glassdoor. As per the reports of U.S. Bureau of Labor Statistics, the demand for data science skills will bring a 27.8percent rise in employment by 2026. The demand for Data Scientists is astonishing and greater, but there is a lack of professional Data Scientists. Data science, Research, and analytics are now available to businesses with the aid of automation and education. Many training programs are being conducted to provide Data Scientists to the business. Data Science is widening and is being adopted by many companies to gain a competitive edge and generate revenue by improving production. To be at the forefront in this data-driven world, industries require professional Data Scientists with strong technical skills.
- Computer Skills
- Basic Mathematical Concepts
- Analytical Mindset
Data Science Training Program Overview
The Data Science Using Python and R is a truly transformative program which takes the students from novices to job-ready candidates in this competitive US market. The defining feature of this program that it aims to blend scientifically rigorous and multidisciplinary knowledge into easily understandable concepts. This course aims to be a primer and will attract learners from diverse domains such as Business Professionals, Programmers, Researchers, Academia, Industry practitioners among others. Students will begin by learning the basics of statistics and slowly progress to learning more complex algorithms in the data science toolkit such as regression, tree-based methods, supervised and unsupervised algorithms.
Data Science Courses Learning Outcomes
Data Science course using R and Python training in the USA is designed to build the workforce what the current market needs. This course helps students and professionals to acquire the knowledge of Data Science and all the technical skills required. 360DigiTMG offers a Data Science course in USA which focusses to train the aspirants with industry use cases so that the students can gain in-depth knowledge of the applications of tools. Data Scientist job is considered as the sexiest job of this century, this course enables students to grab lucrative jobs and achieve their goals. The course includes basics and advanced versions of Data Science. The training is delivered by industry experts who have exceptional experience and a team of dedicated mentors who guide students throughout their learning journey.
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Who Should Sign Up?
- Candidates aspiring to be IR 4.0 consultants
- CEOs and other senior executives
- Plant heads, Operations heads, Line managers
- Employees of organisations that are planning to implement smart factories
- Research and Development heads and Industrial engineering heads
- Professionals who want to be a part of an organisation’s change management
Modules for Data Science Course
The modules of the Data Science course are designed meticulously as per the business trends. Much emphasis is placed on algorithms, concepts, and statistical tools. Python is considered to be the most important programming language and the data scientists have to be pro in using Python. The module introduces descriptive analytics, Data mining, Data visualization, Linear regression, and Multiple Linear regression. Students will learn about Lasso, Ridge, and logistic regression. Learn predictive modeling which is very important and useful. Learn various concepts like Machine Learning algorithms - the K-nearest neighbor algorithm which could be used both for classification and regression. The decision tree algorithm is a popular non-linear tree-based algorithm. Furthermore, the module introduces the concept of Bagging which is a type of ensemble technique and Random Forest algorithm which is a type of bagging algorithm. Students will learn the Naive Bayes model which is based on the Bayesian Probability Technique. This algorithm has been successfully deployed to detect spam with great accuracy. The other modules explain the difference between ANN and Deep Learning is that the network in deep neural networks consists of multiple hidden layers vs just a single layer in the ANN. Learn the concept of a time series and techniques to deal with time-series data such as AR, ARMA, and ARIMA models. Students will also learn the latest techniques called Black box and Support vector machines. This course is delivered with real-time projects, students gain hands-on experience and will able to perform with confidence. This type of training helps to build technical knowledge among the students and prepare them to face real business challenges.
CRISP-DM stands for Cross-industry standard process for data mining and is the bedrock framework of any data science project. This module will explain in detail the cyclical process and all the phases of this methodology. The phases are:
- Business Understanding
- Data Understanding
- Data Preparation
Exploratory Data Analysis is usually where the data scientists tend to spend most of their time in the project. This often involves understanding the dataset, summarizing and describing the data at hand.
This is the phase where the statistical techniques are applied to draw inferences from the data. Typically, there are a lot of statistical techniques that are leveraged during this module and applied to the data.
This module introduces the various visual plots such as the bar plot, histogram, scatter plot, box and whiskers plot, etc., and how they can be leveraged to identify patterns and correlations in the data.
This module introduces the basic concepts of probability distributions and how that knowledge is extensively applied in data science projects. The various types of distributions like Gaussian (Normal), Bernoulli, Poisson, Binomial, Multinomial, etc.
This module introduces the concepts of Hypothesis testing and defines in detail about Null and Alternative Hypothesis and the scenarios where each of them could be proved. It discusses in detail the concepts like parametric tests like 1 sample t-test, 2 sample t-test, ANOVA and nonparametric tests like Chi-Square tests.
This module marks the beginning of predictive analytics and lays the foundation for the rest of the course modules of Machine Learning. The module introduces one of the oldest and simplest supervised learning techniques - the linear regression (ordinary least squares).
As can be easily understood, this is an extension of the OLS Linear Regression, by expanding the input space into multiple linear outputs. We discuss this using one of the most commonly used datasets and learn how it can be implemented in Python and R programming languages.
This module discusses the other advanced and useful types of regression models - Lasso and Ridge regression. We discuss the pros and cons of each of these models with practical examples using Python and R.
Also called the Logit model, this algorithm predicts the probability of the event falling into a certain category - Pass/Fail, Fraud/Not Fraud, Yes/No etc. We also get introduced to the concept of maximum likelihood estimation techniques which form the regression coefficients.
This is an extension of the Logistic Regression - the only difference being the output class could have more than 2 classes.
Count data is a data type in which the observations can only consist of non-negative numbers (0,1,2, 3 …). This module introduces regression models for such data such as Poisson Regression and Negative Binomial Regression and their application in survival analytics.
This module provides a general overview of unsupervised learning before launching into one of the most famous of its techniques - Clustering. Multiple algorithms such as connectivity based (Hierarchical), centroid based (k-Means), Distribution based, etc are discussed and implemented along with real- life scenarios.
This module discusses another great technique called the Principal Component Analysis. It is one of the dimensionality reduction algorithms which answers the question - how do we reduce the feature space without losing a lot of information.
This is a rules based machine learning algorithm where the algorithm learns interesting patterns in data and attempts to answer the question - what goes with what. Different algorithms such as Apriori, FP-growth are discussed and implemented.
This module builds on the concepts of association rule learning and implements a recommendation engine. A great example could be the recommendation engine as seen in Amazon or any other e-commerce platform that suggests items based on your current or previous selections.
This module builds on the fundamentals of Graph Theory and delves into the architecture and implementation of any network with emphasis on Social Networks. The module extensively covers the modelling and visualization of networks and some practical implementations.
This module introduces machine learning algorithms by discussing one of the most popular algorithms - the K-nearest neighbor algorithm which could be used both for classification and regression.
This module introduces the decision tree algorithm which is a popular non-linear tree based algorithm. Furthermore, the discussion then introduces the concept of Bagging which is a type of ensemble technique and Random Forest algorithm which is a type of bagging algorithm.
This module builds on the Bagging algorithm introduced earlier and also adds another ensemble technique called Boosting. The theory behind both the ensemble techniques is discussed in detail.
This module introduces Gradient Descent and explains how it is combined with boosting to achieve gradient boosting. Then we discuss the advanced implementations of gradient descent such as AdaBoost and Extreme Gradient Boosting.
All the prior modules have been dealing with structured data (data that can be stored as rows and columns and that follow relational requirements). This module introduces the unstructured data in the form of text data and provides some tools and techniques to analyze it.
This module discusses another important algorithm - Naive Bayes model which is based on the Bayesian Probability Technique. This algorithm has been successfully deployed to detect spam with great accuracy.
This module discusses the perceptron, which is the fundamental concept of an artificial neural network. The basic version of an ANN called the multi layer perceptron (MLP) is also discussed.
This module discusses the various building blocks of neural networks - perceptron, backpropagation, activation functions, dropout, dense vs sparse layers ,etc.
The difference between ANN and Deep Learning is that the network in deep neural networks consists of multiple hidden layers vs just a single layer in the ANN. The architecture is explored and some guidelines are laid out.
This module discusses another Black Box technique - Support Vector Machines.
This module builds on the concepts from Module 12 and discusses how survival analysis is performed in practical and real-life scenarios.
This module introduces the concept of a time series. All the previous modules (except text data) were longitudinal or cross-sectional datasets. There is no temporal component in the data (or it is ignored for the purposes of algorithm). This module introduces techniques to deal with time-series data such as AR, ARMA and ARIMA models.
This module builds on the concepts previously discussed and delves into some of the data-driven algorithms available to address the time-series problems such as MA, EMA and Econometric Models.
Data Science Trends in USA
There will be massive growth in the field of Data Science in USA. Many new technological advancements will take place in AI and Machine Learning. There is a huge amount of data everywhere that has to be managed and utilized for valuable insights which lead to the generation of revenue and improve productivity. We need to keep updated with the latest and popular trends in Data Science. Let’s check out a few popular trends in Data Science. Big data is evolving tremendously. Many companies are adopting Big Data Analytics which helps them to gain a competitive edge and achieve their goals. The programming tool Python is used to analyze big data. Along with this predictive analysis helps in identifying the occurrence of future events and take action on it. This predictive analytics helps to identify the choices of your customers and helps to build smart strategies to target new customers and retain existing customers. The other popular trend is IoT, as per the reports by IDC, the investment in IoT will reach up to $1.5 trillion by the end of 2020.
Many smart devices like Google Assistant, Amazon Alexa, or Microsoft Cortana are built based on IoT technology. IoT is grabbing much attention and will stay for a long time. The next popular trend in Data Science is Edge Computing. It is considered to be the alternative for Big Data Analytics. Edge Computing combined with Cloud technology can provide an organized structure that helps in minimizing risks. We will be witnessing major innovations in Artificial Intelligence and Machine Learning by the end of 2020. Many apps will be developed with AI and other technologies which improves the mode of work. Automated Machine Learning will take over the market and draws much improvement and reduces human errors. So we can say that Data Science is going to stay and rule the world and there would be a constant demand for professional Data Scientists.
How We Prepare You
- Additional Assignments of over 140 hours
- Live Free Webinars
- Resume and LinkedIn Review Sessions
- 3 Month Access to LMS
- 24/7 support
- Job Assistance in Data Science Fields
- Complimentary Courses
- Unlimited Mock Interview and Quiz Session
- Hands-on Experience in a Live Project
- Life Time Free access to Industry Webinars
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