Course Description

Python is a general-purpose programming language that is becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. Unlike any other Python tutorial, this course focuses on Python specifically for data science. In our Intro to Python class, you will learn about powerful ways to store and manipulate data as well as cool data science tools to start your own analyses

Data Science and Machine Learning

This course will enable you to gain the skills and knowledge that you need to successfully carry-out real-world data science and machine learning projects.

The first part of the course covers data analysis and visualization. You will be working on real datasets using Python’s Numpy, Pandas, Matplotlib and Seaborn libraries.

The second part of the course focuses on machine learning. We will be covering both supervised and unsupervised learning. We will be working on case studies from a wide range of verticals including finance, heath-care, real estate, sales, and marketing. Some of the algorithms that will be discussed include Linear Regression, Logistic Regression, Support Vector Machines (SVM), and K-means clustering. This course is the foundation for Deep Learning courses in this specialization.

Course Details

Becoming a Data Science Engineer puts you on the path to an exciting, evolving career that is predicted to grow sharply into 2025 and beyond. Data Science will impact all segments of daily life by 2025, with applications in a wide range of industries such as healthcare, transportation, insurance, transport and logistics and even customer service. The need for Data Science specialists exists in just about every field as companies seek to give computers the ability to think, learn and adapt.

Foundations of data science
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1. Intro to data science 
2. Math for data science
3. Statistics for data science(2 lectures)
---> 4 lectures

Python
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1. Intro to computing 
2. Intro to python programming
3. Variables, data types and strings
4. List, tuple and dictionaries
5. Conditionals
6. Looping
7. Functions and scopes
8. Modules and Exception handling
9. Working with files in python
10. Working with APIs in python
11. Object oriented concepts in python *(1/2 lectures)
12. Basic SQL lesson *(1/2 lectures)
---> 12 lectures

Data Analysis
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1. Numpy(2 lectures)
2. Pandas(2 lectures)
3. Matplotlib and Seaborn(2 lectures)
4. EDA stackoverflow(2 lectures)
---> 8 lectures

Machine Learning
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1. Linear regression theory(4 lectures)
2. Linear regression with scikit-learn(2 lectures)
3. Logistic regression with scikit-learn(2 lectures)
3. Decision tree(2 lectures)
4. Random Forest(2 lectures)
5. Gradient boosting(2 lectures)
6. Unsupervised learning: clustering, k-means, recommender systems(2 lectures)
---> 16 lectures
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Total = 40 Lectures

                                    Best of Luck