Data Science with Python Course

Data Science with Python Course Overview

The Python Data Science course teaches you to master the concepts of Python programming. Through this Python for Data Science training, you will gain knowledge in data analysis, machine learning, data visualization, web scraping, & natural language processing. Upon course completion, you will master the essential tools of Data Science with Python.

Course Curriculum

Eligibility

The demand for Data Science professionals has surged, making this course well-suited for participants at all levels of experience. This Python for Data Science training is beneficial for analytics professionals willing to work with Python, Software, and IT professionals interested in the field of analytics, and anyone with a genuine interest in Data Science.

Pre-requisites

To best understand the Python Data Science course, it is recommended that you begin with the courses including, Introduction to Data Science in Python, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science. These courses are offered as free companions with this program.

Course Benefits

Data Science is an evolving field and Python has become a required skill for 46-percent of jobs in Data Science. The demand for Data Science professionals will grow an estimated 1581-percent by 2020 and professionals with Python skills will have an additional advantage.

Industry Project

1. Products rating prediction for Amazon
   Help Amazon, a US-based e-commerce company, improve its recommendation engine by predicting ratings for the non-rated products and adding them to recommendations accordingly.


2. Demand Forecasting for Walmart
   Predict accurate sales for 45 Walmart stores, considering the impact of promotional markdown events. Check if macroeconomic factors have an impact on sales.


3. Improving customer experience for Comcast
   Provide Comcast, a US-based global telecom company, key recommendations to improve customer experience by identifying and improving problem areas that lower customer satisfaction.


4. Attrition Analysis for IBM
   IBM, a leading US-based IT company, wants to identify the factors that influence employee attrition by building a logistics regression model that can help predict employee churn.


5. NYC 311 Service Request Analysis
   Perform a service request data analysis of New York City 3-1-1 calls. Focus on data wrangling techniques to understand patterns in the data and visualize the major complaint types.


6. MovieLens Dataset Analysis
   A research team is working on information filtering, collaborative filtering, and recommender systems. Perform analysis using Exploratory Data Analysis technique for user datasets.