Materiais

Conteúdo Programatico

INTRODUCTION TO COMPUTER PROGRAMMING

1. Getting Started with Python
2. Data Types in Python
3. Indexing & Slicing
4. In-built Functions and Methods
5. Conditional Statements
6. Loops & Iterations
7. Conditional & Infinite Looping
8. Advanced Looping Concepts
9. Custom functions in Python
10. Lambda & Map Functions
11. Errors and Exception Handling
12. OOPs in Python
13. Coding Best Practices
14. Arrays and Strings
15. Recursion - I and II
16. Sorting Algorithms
17. Search Algorithms
18. Competitive Coding

NUMERICAL PROGRAMMING IN PYTHON

1. Packages & Libraries - OS
2. Datetime, Regex & Beautiful Soup
3. Command Line & File System
4. Git and Github
5. Standard Data Management Libraries
6. Data Wrangling using Pandas and Numpy
7. Data Visualisation Libraries - Matplotlib & Seaborn
8. Exploratory Data Analysis

RELATIONAL DATABASES

1. Getting Started with SQL
2. SQL Environment & Basic Commands
3. Fundamentals of SQL Query
4. Dealing with Multiple Tables
5. Advanced SQL Joins
6. Mathematical & Data type conversion Functions
7. DateTime & String Functions
8. Window Functions
9. Miscellaneous Functions
10. Connect & Analyse Data with SQL & Python
11. Database Management & Schema Design
12. Competitive Coding & Query Optimisation
13. Complex queries using CTE & Pivoting
14. Type Casting & Math Functions
15. Advanced SQL Joins
16. Type Casting & Math Functions

DATA VISUALIZATION TOOLS

1. Fundamentals of Excel
2. Data Exploration with In-Built Functions
3. Storytelling with Excel
4. Advanced Dashboarding Concepts - Macros & VBA
5. Getting Started with Tableau Ecosystem
6. Choosing the Right Chart - Visual Intuition
7. Storytelling with Tableau
8. Intro to Power BI
9. Intro to Google Analytics
10. Dashboarding and SQL

APPLIED STATISTICS

1. Calculus for ML
2. Vector Algebra
3. Matrix Algebra
4. Probability Theory
5. Data Summarization
6. Probability Distributions - Discrete and Continuous
7. Joint Distribution
8. Sampling and Statistical Inference
9. Concept of Confidence
10. Hypothesis Testing
11. Statistical Inference in Industry - A/B testing

INTRODUCTION TO MARCHINE LEARNING

1. Getting Started With ML
2. ML Lifecycle
3. Implementing simple Supervised Algorithm
4. Linear & Tree based models
5. Implementing simple Unsupervised Algorithm
6. Unsupervised Clustering: K-means & Hierarchical
7. Data Preparation for ML Models
8. Cross validation
9. Hyperparameter tuning
10. TedX Views Prediction - Case Study
11. Customer Segmentation - Case Study
12. Time Series Analysis
13. Bagging & Boosting: Complex Algorithms
14. Nonlinear Algorithms - Polynomial Regression
15. SVM & Neural Networks
16. Natural Language Processing
17. Image processing
18. Recommender Systems
19. SQL Feature Engineering, Prediction and Analysis

DISTRIBUTED MACHINE LEARNING

1. Big Data Fundamentals
2. Data Warehousing with Hive
3. Apache Spark using Python
4. Distributed ML Training

PRODUCT ANALYTICS

1. Product Analytics Essentials
2. Core Visualisation Principles
3. Product Intelligence Platforms
4. Advanced Query Optimisation
5. Business Process Automation

DEEP LEARNING

1. Deep Neural Networks
2. Natural Language Processing
3. Computer Vision