enjoyalgorithms

Data Science Interview Course

A self-paced data science course for students and professionals who are looking to apply data science to industrial datasets, extract insights, and build a promising career in data science.

What will you learn?

✓ Data Science Fundamentals

✓ Data Analysis Techniques

✓ Python and ML Fundamentals

✓ Projects with Python Code

enjoyalgorithms data science course

Data Science Course Modules

Mentor and Author: Ravish Kumar

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Python Basics (Module 1)

1.

Introduction to Data Types

2.

Tuples and Lists

3.

Sets and Dictionaries

4.

Conditions and Branching

5.

Introduction to Loop

6.

Introduction to Functions

7.

Introduction to OOPS

8.

Introduction to Numpy

9.

Introduction to Pandas

Kickstart to Data Science (Module 2)

1.

What is Data Science?

2.

Guide to Become a Data Scientist

3.

Installation of Jupyter-notebook

4.

Introduction to Big Data

5.

Introduction to Hadoop

6.

Introduction to APIs

7.

Introduction to SQL

8.

Introduction to Databases

Data Analysis, Pre-processing and Visualization (Module 3)

1.

Introduction to Data Visulaization

2.

Introduction to Matplotlib

3.

Introduction to Seaborn

4.

Introduction to Data Pre-processing

5.

Data pre-processing hands-on

6.

Exploratory data analysis

7.

Feature Engineering

8.

Feature Selection Techniques

9.

Anomaly Detection

Understanding Various forms of Data (Module 4)

1.

Need of Feature scaling

2.

Introduction to Regex

3.

Applications of Regex

4.

Text Data Pre-processing

5.

Vector Encoding

6.

Pre-processing Time Series Data

7.

Handling Data-time with Python

Machine Learning Concepts (Module 5)

1.

Introduction to Machine Learning

2.

How exactly machine learns?

3.

Gradient Descent Algorithm

4.

Supervised vs Unsupervised

5.

Classification vs Regression

6.

Introduction to Regularization

7.

Evaluation for Classification

8.

Evaluation for Regression

9.

Introduction to Scikit-Learn

Popular Machine Learning Algorithms (Module 6)

1.

Linear Regression

2.

Logistic Regression

3.

Support Vector Machines

4.

K-means Clustering Algorithm

5.

Principal Component Analysis

6.

t-SNE Algorithm

7.

Hierarchical Clustering

8.

Apriori Algorithm

Data Science Projects (Module 7)

1.

Life Expectancy Prediction

2.

Employee Attrition Rate Prediction

3.

Boston House Price prediction

4.

Image Compression using PCA

5.

Personality Prediction

6.

Music Recommendation System

7.

Time Series Forecasting

8.

Customer Churn Prediction

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