Data Science & Machine Learning CourseDesigned for Working Professionals

Curriculum

Module 1 (8 Weeks) – Introduction to Programming

  • Decision trees & control
  • Binary number system
  • Strings
  • Arithmetic operators
  • Loops

Module 2 (8 Weeks) – Programming Constructs

  • Functions
  • Recursions
  • Pointers
  • Structures
  • Unions
  • Dynamic Arrays
  • Asymptotic notations

Module 3 (15 Weeks) – Problem Solving & CS Fundamentals

  • Time Complexity
  • Arrays & Strings
  • Binary Search & 2 Pointers
  • Recursion, Hashing & Sorting
  • Bit manipulation
  • Stacks, Queues & Linked Lists
  • Trees, Tries, Heap & Greedy
  • DP, Graphs
  • DB, OS & Computer Networks

Module 4 (8 Weeks) –  Statistical Analysis & Data Analytics

  • Python, Jupyter, Numpy, Pandas
  • Git, Linux Terminal, File I/O
  • Statistics, Probability, Linear Algebra
  • Distributions, Sampling, Hypothesis Testing
  • Databases, SQL, Index, Partition, Schema
  • Web API, Scraping, Automation, Flask

Module 5 (8 Weeks) –  Data Science & Machine Learning

  • EDA, Data wrangling, Feature Engineering
  • Supervised & Unsupervised Models
  • Ensembling
  • Factor analysis
  • Predictive Modeling & Forecasting
  • Recommender system

Module 6 (15 Weeks) –  ML Engineering – Deep Learning & Big Data

  • Keras, TensorFlow, PyTorch
  • Neural Networks, Computer Vision, NLP
  • Reinforcement Learning
  • Research Papers in Deep Learning
  • Warehouse – AWS S3, HDFS, HBase, NoSQL
  • Ingestion – Kafka
  • Analysis – PySpark, YARN, Airflow, Hive