DataScience Content

Data Science Content

Track 1:- Python for Data Science (40 Hours)

A. Python for beginners

Syntax
Variables
Data types (Numbers - Strings - Lists - Set, tuples and Boolean - Dictionary)
Flowchart
Input & output
Comparison operators
Conditional statements
Loops (while/for loops)
Python built-in functions (enumerate, zip, ...)
List|Dict Comprehensions
Mini-project #1 (Calculator)
Mini-project #2 (Guess the Number Game)
Functions
Scope of variables (Local/Global)
File Handling
Lambda Expressions
Map, Reduce, Filter using Lambda Expression
Error Handling
Introduction to classes
OOP Concepts (Encapsulation, inheritance, abstraction, polymorphism)
Converting Jupiter notebook to .py
Importing .py & packages
OS package handling
Git & GitHub
Mini-project #3 (TicTacToe)
Mini-project #4 (Rock Paper Scissors)
Python Final Project (Classes using 4 Mini-projects)

B. Databases (SQL--MYSQL)

Introduction to Database
Relational Database (Primary key - Foreign Key - Entity Relationships)
SQL Syntax
DDL (Create Database & Tables - Alter - Drop - Truncate)
Data Manipulation (Select - Insert - Update - Delete)
Query Language (Select - Where - Order By - Group By ... etc)
Database Project (Company Usecase)
Connect database to python
Bonus Video (Python TKinter for GUI)
Bonus Task (Create a GUI interface for Company Database)

C. Math & Statistics for Data science

Getting started with statistics
Data Classification
Standard Deviation
Correlation and Covariation
Introduction to Probability
Hypothesis Testing
Marginal and Conditional Probability
Normal Distribution
Linear Algebra (Matrix Calculations)

D. Python packages for Data analysis

Numpy
Pandas
Matplotlib
Seaborn

E. Data Importing & Cleansing

Importing from different sources of data using Pandas
Handling Missing and Invalid Data
Cleansing Missing Data Using Pandas Python Package
Cleansing the outlying data using Pandas Python Package
Missing Data Imputation
Categorical Variable Encoding

F. Exploratory Data Analysis using Python

Visualization Options
Data Modelling
Introduction to PowerBI

G. Exploratory Data Analysis

Data Visualization using Python Packages
Project #1: TMDB Movies Dataset Analysis
EDA Using PowerBI
Project #2: Udemy Courses Dataset Analysis

Track 2: Machine Learning Specialist Course (100 Hours)

I. Machine Learning

Introduction to Machine Learning Fundamentals
Classification Models

K-Nearest Neighbor (KNN)
Logistic Regression
Precision, Recall & F-score
AUC & ROC
Support Vector Machine (SVM)
Native Bayes
Cross Validation
Grid Search
Under Sampling and Oversampling (Overfitting & Underfitting)
Balanced and In Balanced Data
Decision Tree Classification
Random Forest Classification (Ensemble Method, Bagging and Boosting)
Confusion Matrix
Project #3: Breast Cancer Wisconsin Classification.
Project #4: UCI Heart Disease Classification. to PowerBI

3. Regression Models

Linear Regression (Feature Selection, Gradient Descent,
Ordinary Least Squares)
Multiple Linear Regression
Bias and Variance
Overfitting and Underfitting
MSE, MAE, R-Square
Polynomial Regression
Ridge Regression
Lasso Regression
Elastic Net Regression
Support Vector Regression (SVR)
Decision Tree Regression
Random Forest Regression
Project #5: Diamonds Dataset Regression.

4. Unsupervised Learning (Clustering)

K-Means & Hierarchical Clustering (Mall problem)
DBSCAN (Density based Clustering).
Project #6: Breast Cancer Cluster Analysis.

5. Model Selection & Boosting

K-fold Cross Validation
Parameter Tuning
Grid Search
XGBoost

6. Dimensionality Reduction

PCA

J. Deep Learning

Introduction to Deep Learning
Introduction keras & tensorflow
Artificial Neural Networks
Convolutional Neural Networks (Computer Vision)
Project #7: Fashion Mnist Image Classification.
Recurrent Neural Networks
LSTM
Project #8: Apple Stock Price Prediction.

K. Introduction to NLP

Project #9: Building Word Embeddings Using Word2Vec

L. Bonus Project: APIs with Flask & Postman

M. Final Capstone Project