Course Duration: 4 months (29 classes, 2 classes per week)
| # | Class 1-2 | Details |
|---|---|---|
| 1 | Introduction to the Course and Artificial Intelligence, Use cases of AI, Introduction to Python. | A glimpse of AI, what are the career paths, What I do in this space and so on,
Setting up Environment, Syntax (Variables, Inputs, Datatypes, If-else, Mathematical Operations), Loops, Functions, Break, Continue, Pass Keywords. |
| # | Class 3-4 | Details |
|---|---|---|
| 1 | Python Data Structures & Key Functions, Parallel Lists, Exception Handling, List Comprehension, List Slicing |
Data Structures (List, Arrays, Set, Dictionary, Tuple), Map, Apply, Filter, Regex & Recursion,
Parallel Lists, Exception Handling, List Comprehension, List Slicing + Assignment Discussion |
| # | Class 5-6 | Details |
|---|---|---|
| 1 | Data Extraction, Data Extraction ++ | Playing with CVSs, JSON, RSS feeds, Data Scraping, Extraction via Restful APIs |
| # | Class 7-8 | Details |
|---|---|---|
| 1 | Intro to Pandas,
Intro to NumPy + Seaborn + SciPy |
Merge, concat, dropping duplicates, removal of Nulls, filtering records etc) with a real-world
dataset, feature engineering
NumPy arrays & some handy functions, Analytical library used in plotting different interactive graphs. SciPy |
| # | Class 9-10 | Details |
|---|---|---|
| 1 | Introduction to SQL
Introduction to SQL ++ |
DDL, DML, WHERE Statements, Different Types of Joins, SubQueries Union, Intersect, Triggers, Stored Procedures, Wildcards and CTEs. |
| # | Class 11-12 | Details | |
|---|---|---|---|
| 1 | Data Engineering Pipeline Data Engineering Advance | Deploying a full fledge pipeline which includes Data Extraction, Cleaning, Validations and Ingestion Introduction to T-SQL, Hadoop, Spark, Airflow, Databricks, Cloud (Azure, AWS, GCP) |
| # | Class 13-14 | Details |
|---|---|---|
| 1 | Data Engineering Advance Assignment 1,2,3 Solution Discussion |
Datawarehouse, DataMart’s, OLTP vs OLAP, ELT vs ETL, SIT vs STAGE vs PROD |
| # | Class 15-16 | Details |
|---|---|---|
| 1 | Introduction to Statistics Statistics ++ | Basic Statistics - Plan to have a session by an external resource ANOVA Test, Chi-Square - Plan to have a session by an external resource |
| # | Class 17-18 | Details |
|---|---|---|
| 1 | Introduction to Machine Learning Linear Regression, Types of Regression | Supervised, Unsupervised, Reinforcement Learning with examples How it works, in what problems we use this? How to interpret and a demo notebook where we'd extracted results with Linear Regression |
| # | Class 19-20 | Details |
|---|---|---|
| 1 | Overfitting & Underfitting, Cross validation Train-test split, Hyperparameters tuning | Concepts + examples and solutions to each problem Different train-test split variants and types of Hyper-parameters tuning |
| # | Class 21-22 | Details |
|---|---|---|
| 1 | Machine Learning Models Confusion Matrix & Classification Report | Logistic Regression, SVM, Decision Trees etc How a model is correct acc to our use-case, will discuss different metrics |
| # | Class 23-24 | Details |
|---|---|---|
| 1 | Associative Rule Mining Introduction to Deep Learning | FP-Tree, Apriori Algos | A small glimpse of Nueral Networks, CNN, RNNs, LSTM and GANs etc so that they have idea about it |
| # | Class 25-26 | Details |
|---|---|---|
| 1 | Dashboard (PowerBI or Looker or Tableau) Business Use case | External Person will come to teach this A hands-on approach which will enhance Cognitive Skills that how we can think like a business and deliver some solutions to them |
| # | Class 27-28 | Details |
|---|---|---|
| 1 | Upwork Session Capstone Project | How to win clients at Upwork as Data Analyst, Data Engineering and Machine Learning niche A project in which all concepts will be revised through coding. |
| # | Class 29 | Details |
|---|---|---|
| 1 | Project Presentations | Reviewed by External Members |