Data Science Course in Chennai
360DigiTMG offers you a data science course in chennai like no other!Propel your data science career forward with the twin engines of Python and R programming. Enroll in our " Data Science using Python and R programming" course and develop codes in Python and R for machine learning and deep learning algorithms. Employ statistics and probability in machine learning solutions. Perform predictive modeling with regression analysis. Develop algorithms for neural networks, forecasting and time series analysis in the best Data Science training course in Chennai.
On-campus training: 120 hours
- Computer Skills
- Basic Mathematical Concepts
- Analytical Mindset
Data Science Training in Chennai
This data science course begins with an introduction to statistics, probability, python and R programming and exploratory data analysis. Participants learn to perform Data Mining Supervised with Linear regression and Predictive Modelling with Multiple Linear Regression techniques. Data Mining Unsupervised using Clustering, dimension reduction and association rules is also dealt with in detail. A module is dedicated to scripting machine learning algorithms and enabling deep learning and neural networks with Black Box techniques and SVM. Learn to perform proactive forecasting and time series analysis with algorithms scripted in Python and R in the best Data Science training institute in Chennai.
Data Science Training Learning Outcomes in Chennai
Data Scientist Course Training Modules in Chennai
Project Management insights need to be learnt for the implementation of any analytics projects. Cross-Industry Process for Data Mining (CRISP-DM) methodologies are used for Data Analytics projects which are broadly explained in 6 stages. You will be introduced to the tasks performed in these 6 stages to successfully develop and deploy an Analytics solution.
Understand the business problem and map the problem objectives with the Data provided to derive insights. Learn to perform Descriptive Analytics and understand the concepts of Data Preparation, Data Cleansing, Feature Engineering, Imputation, etc. as a part of this module.
Learn to draw insights by applying statistical calculations on the Data. Business moments calculations will yield information on the raw data. Understand about these business moments calculations and the insights they derive. Learn how Descriptive Analytics can be better performed by visualizing the details for storytelling.
In this tutorial, learn to intercept the details each of the plots explain about the Data. Understand the Pros & Cons of each technique and learn to choose the appropriate technique to be used in different scenarios. Learn how to plot using functions of Python and R. Understand the art of estimation and inference answers with confidence for business problems based on the small data obtained by sampling on population. Learn about the difference between Parameters and Statistics and understand the process of Inferential Statistics.
The chance of the estimated value is called probability, in this tutorial, you will revise the basic mathematical concepts of probability and its calculations. Understand to intercept the spread of the probability to estimate value with confidence. A Probability Distribution is a pattern in the Data and you will learn to interpret the distribution of the Data using examples.
Hypotheses testing is the process of making assumptions and testing the same for a business problem. In this module, learn the rules to make assumptions and understand the flow of performing the tests to evaluate these assumptions in different conditions. Learn the conditions and errors that may arise while performing the hypothesis for a business condition. You will learn to choose the appropriate hypothesis testing based on Data and Business Problems.
Predictive Analytics helps in estimating a value for a condition upfront to assist the businesses to brace for the future. Under the Data Mining Process, Supervised learning concepts are used for Predictions. Learn about the explainable Machine Learning Technique called Regression.
You will learn about the Bi-Variate Analysis using a Scatter Plot and Correlation Analysis to interpret the relationship between variables. Understand the concept of the straight-line equation and its usage for the prediction of a dependent variable.
In this tutorial, you will learn the prerequisites and post-requisites for fitting a linear model for the Data. Understand the challenges in constructing a linear model to regress a dependent variable in a multi-dimensional space. You will learn to deal with Collinearity conditions, Heteroscedasticity conditions. You will also learn how to improve the accuracy of the prediction models.
Understand the Model Evaluation Techniques using Error Function. Learn about the different levels of accuracy levels for the models. In this module, you will learn the conditions of Overfitting and Under-fitting. Understand the regularization techniques L1 and L2 to handle variance and bias by penalizing the coefficients.
In this tutorial, you will learn about the Binary Value Prediction based on a Linear Model. It is the simplest approach for binary classification problems among the Machine learning Algorithms using Maximum Likelihood Estimate (MLE) technique. You will learn how the Logistic Regression will predict the binary outcome by using cutoff value with probability values. Understand the Model Evaluation Technique using Confusion Matrix along with other metrics collected to improve the model.
Classification model for predicting multiple categorical Data which is based on probability calculation similar to logistic regression. In a logistic regression model, you will learn to predict a binary outcome, whereas if the outcome has more than 2 categories then multinomial regression is used. Understand the difference between the types of logistic regression models and learn about multi logit function.
Learn to work with count data using these advanced regression techniques. Linear Models are used in the case of continuous and binary dependent cases, where generalized linear models are applied for positive discrete data value predictions. Learn about discrete data distributions and techniques to predict them. You will learn about Poison and Negative binomial models and learn about the conditions on when to use them.
Clustering is a process of segregating the homogeneous records in the Data. Data Mining unsupervised learning techniques are used to identify the pattern among the raw data collection.
Clustering helps in deriving homogeneity which in-turn helps in applying simple statistical computing to derive meaningful insights. You will learn how Clustering is different from Prediction Techniques. In this tutorial, you will learn about the different approaches to achieve the data segregation for multivariate data.
High Dimensional Data handling is a complex task. Applying any statistical models on high dimensional data is time-consuming and gives low inaccuracy. In this module, learn about how to deal with high dimensional data by capturing information from all the original attributes into a low dimensional space. You will use matrix computation logic to understand how low dimensional data is equivalent to the original data.
Relationship between entities is analysed in this module. The frequently occurring entities are identified to define the dependency between them. Market Basket Analysis technique is a measure of the relationship between entities. Rules are generated based on statistical measures to derive the dependency. You will learn about the drawbacks in the frequency-based approaches and learn how to efficiently define the best association among the entities by considering independence among them.
Unsupervised learning deals with identifying the patterns in the data. As part of this module, you will learn to find the customer behaviour/pattern based on their history. Making the right suggestions to customers will help organizations to retain them. Understand how to define the pattern using distance metrics to make more meaningful suggestions. Learn to measure the similarity between customers using various methodologies. Understand the pros and cons of each technique to derive these patterns.
Learn about measuring a value for nodes/entities in a network. A network could be a social media network or a business network. Understanding the network is essential to organizations to define new revenue generation areas, optimize the current channels of revenues and identify the grey areas in the business network to get a competitive edge.
Learn how to predict a non-numeric dependent variable. k-NN is a simple machine learning algorithm based on Distance Metrics. You will learn the measure of distances based on k value, and also understand the logic of finding the best value of k for classification. A k-NN Algorithm can be used for both predictions of a numeric value and classification of categorical value. Learn about the packages used to implement k-NN classifiers in Python and R.
The graphical representation of data to create classification rules in the form a tree structure is called a Decision Tree. The tree is grown with information content extracted at each branch node from the root node. It is grown till a Decision or label is identified (leaf node). Statistical measure entropy is used to calculate the information content to split the tree into homogeneous branches. Random Forest is a collection of multiple trees to produce an unbiased solution on the business problem
The Decision Tree classification model is a technique which is most prone to overfitting. To improve the reliability and accuracy of the Decision Tree, Ensemble techniques are applied. Bagging which is a parallel approach and Boosting which is a sequential approach are two most popular methods used to handle overfitting problems in Decision Trees.
The Ensemble Techniques try to enhance weak learners by iteratively repeating the training process with low weights assigned to correctly classified data points and high weights assigned to weakly learnt data points, thereby minimizing the overall error. As part of this module, you will learn the Adaboost and Extreme Gradient Boosting techniques developed on complex data.
Majority of the data generated today is in textual format, thanks to social media and the internet made available to smartphones. In this module, you will learn how to handle the unstructured textual data to derive insights. Learn to convert the unstructured data to structured form using the Bag of Words Method. Understand how to read the data from Word Clouds. Advanced concepts of sentiment analysis using natural language processing is also discussed as part of this module.
Revisit the most famous probability algorithm the Bayes Theorem and its applicability in Predictive Analytics as part of this module. How e-mails can be skimmed for the content and classified as spam or ham will be thought of as a use case. Learn how to prepare the input data from text data and apply probability calculations on this data to derive business value.
Learn how a neural network solves complex data problems using the logic of how the biological brain works. Understand the Perceptron Algorithm as part of this module. You will learn how a Perceptron Algorithm learns to solve a linear classification problem. Understand the various parameters used for learning a Perceptron Algorithm. Learn how to deal with non-linear classification problems.
Understand how a network learns based on integration function and activation function. Understand all the hyper-parameters tuned to train the network and update the weights. Learn about weight calculations, learning rates, error functions optimization techniques to reach the least error.
A neural network is the most popular Deep Learning Algorithm used to work with unstructured data. Learn how to handle images, videos using convolution neural networks. Learn about the finer aspects to deal with images with the computer vision OpenCV package. Learn the RNN a variant of a neural network to deal with sequential data like text or voice. Understand how RNN uses learning from the past layer to predict sequential values.
Black Box Technique SVM is a Deep Learning Algorithm used to solve numeric and categorical data predictions using boundaries to create linearly separable homogeneous groups. Understand how the non-linear multi-dimensional spaces are dealt with Kernel Algorithms to bring them to linearly separable spaces in a higher-dimensional space.
Learn to predict the time/duration for an event. You will learn about the applications of survival analysis in critical decision-making areas in life science, health care, marketing, customer retention, etc. Understand how to deal with censor data and types of censored data. Learn about kaplan meier survival function.
Learn about the skills to forecast the future based on historical data. Understand the systematic and non-systematic components of a time series data. Learn how to interpret the components using plots on time series data. Understand the steps to handle forecasting projects using CRISP-DM project methodology. In this module, you will learn about the forecasting models which are based on regression equations.
Data-driven forecasting models deal with time-series data which have high volatility. These techniques are applied when the past is not equal to the future. Estimating the pattern in time series that is based on the historical data. Understand different types of Smoothing Techniques. You will also learn about the Seasonality Index which is used to derive the variations among the seasons in the series.
The Indian Data Science Market will be worth 6 million dollars in 2025 and data analytics outsourcing industry in India is worth $25 million.
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Who Should Sign Up?
- IT Engineers
- Data and Analytics Manager
- Business Analysts
- Data Engineers
- Banking and Finance Analysts
- Marketing Managers
- Supply Chain Professionals
- HR Managers
- Math, Science and Commerce Graduates
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Data Science Training Panel of Coaches in Chennai
Bharani Kumar Depuru
- Areas of expertise: Data Analytics, Digital Transformation, Industrial Revolution 4.0
- Over 14+ years of professional experience
- Trained over 2,500 professionals from eight countries
- Corporate clients include Hewlett Packard Enterprise, Computer Science Corporation, Akamai, IBS Software, Litmus7, Personiv Alshaya, Synchrony Financials, Deloitte
- Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, (DSDM Atern)
- Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business
Sharat Chandra Kumar
- Areas of expertise: Data sciences, Machine learning, Business intelligence and Data Visualization
- Trained over 1,500 professional across 12 countries
- Worked as a Data scientist for 14+ years across several industry domains
- Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology Infrastructure Library
- Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, Python, Tableau, Cognos
- Corporate clients include DuPont, All-Scripts, Girnarsoft (College-, Car-) and many more
- Areas of expertise: Data sciences, Machine learning, Business intelligence and Data Visualization
- Over 20+ years of industry experience in data science and business intelligence
- Trained professionals from Fortune 500 companies and students at prestigious colleges
- Experienced in Cognos, Tableau, Big Data, NoSQL, NewSQL
- Corporate clients include Time Inc., Hewlett Packard Enterprise, Dell, Metric Fox (Champions Group), TCS and many more
This data science course in chennai was designed to hone your programming skills in Python and R.Showcase this certificate in the job market and win accolades from peers and superiors. The certificate in Data Science using Python and R programming is proof of your diligence and sustained endeavour.It validates your brilliant stint in 360DigiTMG - the best data science training institute in Chennai.
FAQs for Data Science Course in Chennai
It is not advisable to pursue a data science course after 12th standard because most employers expect a minimum qualification of Bachelor's degree in Mathematics/ Statistics/ Computer Science/ Data Science or a Bachelors's degree in Engineering (any discipline). It is advisable to complete your graduation in any one of the above-mentioned disciplines and then join a data science course.
To pursue a career in data science you must have completed a Bachelor's / Master's degree in Mathematics/ Statistics/ Computer Science/ Data Science or a Bachelor's degree in Engineering(any discipline). If you have completed a degree in any of the abovementioned disciplines then you can pursue a career in data science.
India has witnessed a 400% increase in the demand for a data scientist in 2019 while the increase in the supply is only 19%. This means there is a huge potential for job seekers in data science in India. In this scenario, the salaries of data science professionals have skyrocketed and in India, a Data Science professional earns 26% more on an average than a normal software programmer.
The world is adopting Artificial Intelligence and Robotic Process Automation at a rapid pace and definitely, Data Science is the most promising IT career in the future. Therefore the job security and employability of data scientists are very promising.
This is the best time to hop into the Data Science bandwagon!
360DigitTMG offers the best data science course in Chennai. We provide an internship with INNODATATICS wherein the student gets to work on a live project. We provide 100% placement assistance.
SYou can pursue a data science course in Chennai at our franchises locations:
- 360DigiTMG Thoraipakkam
- 360DigiTMG Chromepet
- 360DigiTMG Guduvanchery
- 360DigiTMG Anna Nagar
- 360DigiTMG Porur
The candidate must possess basic mathematical skills and have a foundation in computer science. He must also be of analytical mindset.
You must possess a Bachelors's degree in Mathematics/ Statistics/ Computer Science/ Data Science or a Bachelors's degree in Engineering( any discipline) to be eligible for this course.
The modules in the course curriculum are:
- CRISP-DM Project Management Methodology
- S Exploratory Data Analytics (EDA) / Descriptive Analytics
- Hypothesis Testing
- Data Mining - Supervised and Unsupervised
- Linear Regression and Logistic Regression
- Predictive Modelling using multiple linear regression
- Lasso and Ridge Regression
- Multinomial Regression
- Recommendation Engine
- Network Analytics
- Machine Learning -k NN Classifier
- Naive Bayes
- Text Mining and Natural Language Processing
- Deep Learning Black Box Technique - Neural Network
- Deep Learning Black Box Technique - Support Vector Machine
We offer a scholarship scheme called Jumpstart for deserving candidates. If you qualify for our scholarship scheme you get a 90% fee waiver on the course.
The learning outcomes of this course are:
- Perform Customer Sentiment Analysis with Text Mining
- Learn to interface with various data generation sources and analyze structured and unstructured data
- Understand descriptive and predictive analytics
- Use machine learning algorithms for business decision making
- Build prediction models and perform forecasting for better business decisions
We teach Python, R and R Studio in this course.
Yes, we teach Python and R from a foundational perspective in this course. Hence it is ideal for beginners.
As soon as you join, you can download the course material from our online Learning Management System AISPRY.
We video record all classroom sessions and upload the videos to our online Learning Management System AISPRY. Those who miss a session can download the same from AISPRY.
Once you finish all the classroom sessions and the assignments you will receive a Course Completion Certificate. After this, you can apply for an internship with INNODATATICS. You will get a chance to work on a live project there.
Once a student enrolls in the course, he will be assigned a mentor. If the institute feels he needs additional help, it will assign extra mentors.
The various job roles that one can apply for are:
- Data Analyst: A data analyst deals with Data Cleaning, Exploratory Data Analysis, and Data Visualization. They analyze historical data primarily
- Data Engineer: A data engineer is primarily a programmer who uses Spark, Python and R. He complements the role of a data scientist.
- Data Scientist: Data scientists build algorithms using statistical tools like Python, R, SAS, STATA, Matlab, KNIME, Weka to solve business problems. They also perform predictive modelling.
- Data Architect: They decide on the software and hardware needs during Data Analysis. They have to select and procure the correct database, network infrastructure, memory, GPUs, etc.
- Analytics Manager / Lead
- Machine Learning Engineer
- Statistical Programming Specialist
We offer end -to- end placement assistance in 360DigiTMG. Our placement assistance starts with resume preparation. We then prepare the candidates for interview by conducting several mock interview sessions. We float the candidate's resume to several placement consultants with whom we have a long-standing association. Once the student is placed, we offer technical assistance for the first project on the job.
We have uploaded several free webinars on youtube on Data Science. You can click on the link below to access them:
The average salary of a data scientist in 2019 was Rs.12.6 lakhs ( Source:www.analyticsmag.in)
Sharvin Rao8 months ago
Very good exposure. Satisfied with this program. Teaching materials are complete and does not require any programming background to learn this course
Priya Gopal9 months ago
Very experienced trainer and have patience to deal with every query raised in the classroom.
Lavaniya Rajesveran9 months ago
Great place to learn about Data Science . Trainers are knowlegable and shared lots of new terms which was easily understandable.