Data Science using Python Course

Around 2008 people started hearing term “data scientist”, this term has been used to cover a wide range of functionalities. But data science at the core, is the use of various tools, algorithms, and techniques to identify hidden patterns in large volumes of data. Hence, Python is the top language to work in such scenarios, so, this creates the perfect combo of Python and Data Science.

Python Content

Core Concepts

Module 1: Introduction to Data Science

Week 1: Fundamentals of Data Science

Introduction to Data Science

• What is Data Science
• The Data Science Lifecycle

Python Programming for Data Science (Part 1)

• Introduc8on to Python
• Variables and Data Types

Week 2: Python Basics

• Control flow
• functions
• Data Structures

Week 3: NumPy and Pandas

• NumPy
• Pandas and datasets intro
• Data Manipulation with pandas
• Joining with pandas

Week 4: Data Collection

• Web scraping
• Intro to API and data fetching

Project: Data Fetching project

Module 2: Data Analysis and Statistics

Week 1: Descriptive Staticstics

Measures of Central Tendency and Variability

• Mean, Median, Mode
• Variance and Standard Deviation

Data Distributions

• Normal, Binomial, and Poisson Distributionns
• Probability Density Function

Week 2: Descriptive Statistics (Cont.)

Exploratory Data Analysis (EDA)

• Data Visualization
• Correla8on and Covariance

Exploratory Data Analysis (EDA) (Cont.)

• Outlier Detection
• Data Summary and Interpretation

Week 3: Feature Engineering Tactics

Feature Scaling and encoding

• Feature Scaling and encoding categorical data
• Feature Transformation tools

Feature Engineering continued

• Handling Missing values
• Curse of dimensionality

Week 4: Inferential Statistics and Hypothesis Testing (Cont.)

Regression Analysis (Part 1)

• Simple Linear Regression
• Geometric intuition and formulation

Regression Analysis (Part 2)

• Multiple Linear Regression
• Regression Metrics

Module 3: Machine Learning Fundamentals

Week 1: Supervised Learning

Regression and gradient descent (Part 1)

• Polynomial regression
• Regularization techniques (l1,l2 regression)

Gradient descent (Part 2)

• Gradient Descent from scratch
• Types of Gradient descent

Week 2: Supervised Learning (Cont.)

Logistic Regression (Part 1)

• Binary and Multinomial Logistic Regression
• Odds and Logit

Decision Trees

• Model Evaluation (Confusion Matrix, ROC)
• Model Interpretation

Week 3: Decision Tree and Random Forest

Decision tree

• Collaborative Filtering
• Content-Based Filtering

Intro to ensemble learning and Random Forest

• Ensembles and Voting Ensemble
• Bagging and random forest

Week 4: Unsupervised Learning

Clustering (K-Means, Hierarchical)

• K-Means Clustering
• Hierarchical Clustering

Advanced Clusters

• DBScan Clustering
• SVM and Naïve Bayes Classifiers

Module 4: Advanced Data Science Topics

Week 2: Ensemble learning continued

Stacking

• Stacking and Blending ensemble
• Multi Layered Stacking

Boosting frameworks & Optuna

• Adaboost and XG Boost
• Light GBM & CAT GBM with optuna

Week 3: Deep Learning and Neural Networks

Building Neural Networks with TensorFlow/Keras (Part 1)

• Perceptrons and Activation Functions
• Model Building with TensorFlow

Building Neural Networks with TensorFlow/Keras (Part 2)

• All techniques to improve a neural network

Week 4: Deep learning continued

Day of CNN

• Convolution neural Network
• Intro to Transfer Learning (ResNet , LNet,VGNet)

RNN & LSTM

• A dive in RNN and LSTM
• GRU and deep RNNs