First step to become industry ready for top Data Science job roles.

Registration form   here

At the successful completion of the course student should be able to

• Write basic codes in R for simple data analysis.
• Define, calculate and interpret summary statistics and basic graphs to explore data.
• Perform hypothesis tests; mean, proportion, two population means and proportions, chi-square test of association, correlation.

• Introduction to programming in R: R-studio interface, syntax, entering data and accessing spreadsheet data, writing functions, installing and using libraries.
• Data types.
• Exploring data with summary statistics using R: Mean, median, variance, concept of bias, outliers, and missing values.
• Exploring data with basic statistical graphs using R: Scatter plot, histogram, pie chart, bar charts, multiple bar charts, box-plots etc.
• Introduction to distributions: Binomial, Normal
• Population vs Sample and sampling techniques
• Hypothesis testing 

• The Course 1 would be evaluated based on a mini quiz.

At the successful completion of the course student should be able to

• Prepare a dataset that can be used for analysis using R
• Define and apply basic concepts in database management
• Use SQL and NoSQL to perform simple queries to extract data.

• Data munging with R:
• Introduction to database concepts
• Introduction to SQL
• Introduction to NoSQL

The Course 2 would be evaluated based on a mini quiz.

At the successful completion of the course, student should be able to explain components of basic statistical models, identify and apply suitable models to analyze data.

• Introduction to statistical modeling
• Linear regression model
• Logistic regression model
• Clustering
• Dimension reduction (PCA, FA)
• Time series

The Course 3 would be evaluated based on a mini quiz.

At the successful completion of the course, student should be able to apply basic machine learning algorithms to analyze data.

• Comparison of statistical models and machine learning algorithms
• Comparison of supervised learning vs unsupervised learning
• Cross-validation methods
• Unsupervised learning – Clustering
• Supervised learning – Classification and value prediction: Random forest
• Supervised learning – Neural networks
• Rule based analysis: Apriori, market basket analysis

The Course 4 would be evaluated based on a mini quiz.

At the successful completion of the course, student should be able to apply data visualization concepts to describe data; define big data and discuss basic concepts in big data analytics.

• Data visualization process, do’s and don’ts, Annotations, plots: bubble plots, tree maps etc. using Power BI
• Big data and its applications/case studies
• Cloud computing and internet of things
• Distributed file systems and computing : HADOOP technologies

The Course 5 would be evaluated based on a mini quiz.

At the successful completion of the course, student should be able to use Python programming language for basic data science applications.

• Introduction to python and Jupyter notebook
• Python Basics – Types, Expressions and Variables, String Operations
• Python programming Fundamentals – Conditions and Branching, Loops, Functions, Objects and Classes
• Python Data Structures – Lists, Tuples, Sets, and Dictionaries
• Data analysis, manipulation, and visualization in Python – Numpy, Pandas, Matplotlib, Seaborn 
• Machine learning and deep learning libraries – Sklearn, Tensorflow, Keras

The Course 6 would be evaluated based on a mini quiz.

A certificate will be issued upon successful completion of the course with ≥75% of attendance and a ≥50% score obtained on evaluation.

The participants who successfully complete all six modules are eligible for a capstone project where a dataset will be given to analyze and meet some specific objectives. The participants who meet the minimum requirements (detailed instructions and requirements will be given) will be awarded a specialization certificate in essentials of data science.