Data Science

Data Analytics – Course Curriculum

Unit 1 – Weeks 1-3 :- Excel, Introduction, Text Formatting, Cell Formatting, Conditional Formatting, Operators, Cell Referencing, Auto Fill and Flash Fill, Mathematical and Logical Functions, Nested If Function, Advance Excel, Named Ranges, Data Validation, Lookup Functions, Pivot Tables, Chart and slicers, Dashboarding, Power query.

Unit 1 – Week 4 – Week 5 :- Basic SQL, CRUD – Create, Read, Update, Delete, Primary Key, Foreign Keys, Select , Where, Order by, Group By, Having, Case When, Set Operators, Joins – Inner, Left, Right Joins, LIKE Operator.

Unit 2 – Week 6 – Week 10 :-  Python, Introduction, Computer Programming Data Types, Variables, Basic Input-Output Operations, Basic Operators, Boolean Values, Conditional Execution, Loops, Lists and List Processing, Functions, Tuples, Dictionaries and Sets, Data Processing, Modules, Packages, String and List Methods, Exceptions, Exception Handling, Working with Files, Python for Data Science, Introduction to Numpy, Introduction to Pandas, Data Wrangling in Pandas, Matplotlib.

Unit 3 – Week 11 – 15 :- Guesstimates, Introduction to Guesstimates, Different Approaches, Hacks to solve Guesstimates, Advanced SQL, Joins and Unions, Analytic Functions, Date and String Functions, Nested and Repeated Data, Sub queries, CTEs, Views, Temp Tables, Window Functions, Pivot Tables and Dynamic Variables, Stored Procedures and Triggers, ACID Properties, Indexing.

Tarun Chaudhary

FULL STACK DEVELOPER
12 years of experience

 

Unit 4 – Week 16 – Week 20 :- Case Studies, Introduction to Case Studies, Profitability Framework, Market Entry Framework, Pricing and Abstract Cases, Tableau, Introduction, Basic Visualizations, Sets, Parameters, groups, Calculated Fields, custom visualizations, Dashboards & Stories, Data Storytelling and visual narratives, Probability and Statistics, Statistical Inference, Frequency Distributions, Descriptive Measures, Permutation and Combination, De Morgan’s Law, Simple and Compound Event, Set Theory, Conditional probability, Bayes theorem, Independent events, Random variable, Bernoulli’s random variable, Cumulative Distribution Function,  Expectation and Variance,  Introduction to Binomial experiment, Binomial random variable Mean and variance, Continuation to Continuous Random Variables ,PDF & CDF of continuous RV, Intro to Percentile of a Distribution, Normal Distribution, Standard Normal Distribution, Percentile, Z Notation for Z critical values, Normal to Standard Normal,  Normal Approximation to Binomial Distribution.

Unit 5 – Week 21 – Week 25 :- Machine Learning, Introduction to Machine Learning, Reducing Loss, Tensor Flow & Generalisation, Parameter Tuning and Model Optimization, Regression and Classification, Feature Engineering, Logistic Regression, Regularisation, Decision Tree, SVM, K Means Clustering, Dimensionality Reduction, Gradient Boosting, Artificial Neural networks.

Unit 6 – Week 26 – Week 30 :- 

Revision

Projects

Interview Preparation