Applied Data Science with Python
Applied Data Science with Python
JFH Foundation
Online |
courses
IT/Software Development - DBA/Datawarehousing
Description
About The Course
Establish your mastery of data science and analytics techniques using Python by enrolling in this Data Science with Python course. You’ll learn the essential concepts of Python programming and gain in-depth knowledge of data analytics, machine learning, data visualization, web scraping, and natural language processing.
Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on, Data Science with Python course.
The course will help you develop the right skills needed to be qualified as a Data Analyst.
This course is brought to you by JobsForHer Foundation in association with Simplilearn.
Course Key Features:
- 24 hours of Online self-paced learning
- 44 hours of instructor-led training
- 4 industry-based course-end projects
- Interactive learning with Jupyter notebooks integrated labs
- Dedicated mentoring session from faculty of industry experts
Course Duration: 4-5 weeks
Course Start Date: Shortlisted candidates will be notified via email
Certificate: Upon completion of the course
Program Fees: This course is priced at Rs. 37050/-. But those who are shortlisted for the scholarship will get this FREE of cost
Online or Offline: Online
Who Should Enroll
- Analytics professionals willing to work with Python
- Software and IT professionals interested in analytics
- Anyone with a genuine interest in data science
Prerequisites:
To best understand the Data Science with Python course, it is recommended that you begin with these courses:
- Python Basics
- Math Refresher
- Data Science in Real Life
- Statistics Essentials for Data Science
Takeways
Course Takeaways
This Python for Data Science training course will enable you to:
- Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing; and the basics of statistics
- Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators, and functions
- Perform high-level mathematical computations using the NumPy and SciPy packages and their large library of mathematical functions
- Perform data analysis and manipulation using data structures and tools provided in the Pandas package
- Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline
- Use the Scikit-Learn package for natural language processing and matplotlib library of Python for data visualization
Certification Details and Criteria:
- 85 percent of online self-paced completion or attendance of one live virtual classroom.
- A score of at least 75 percent in course-end assessment
- Successful evaluation in at least one project
Course End Projects:
The course includes four real-world, industry-based projects. Successful evaluation of one of the following projects is a part of the certification eligibility criteria:
Project 1: Products rating prediction for Amazon
Amazon, one of the leading US-based e-commerce companies, recommends products within the same category to customers based on their activity and reviews of similar products. Amazon would like to improve this recommendation engine by predicting ratings for the non-rated products and add them to recommendations accordingly.
Domain: E-commerce
Project 2: Demand Forecasting for Walmart
Predict accurate sales for 45 stores of Walmart, one of the US-based leading retail stores, considering the impact of promotional markdown events. Check if macroeconomic factors, such as CPI and unemployment rate, have an impact on sales.
Domain: Retail
Project 3: Improving Customer Experience for Comcast
Comcast, one of the largest US-based global telecommunication companies, wants to improve customer experience by identifying and acting on problem areas that lower customer satisfaction. The company is also looking for key recommendations that can be implemented to deliver the best customer experience.
Domain: Telecom
Project 4: Attrition Analysis for IBM
IBM, one of the leading US-based IT companies, would like to identify the factors that influence attrition of employees. Based on the parameters identified, the company would also like to build a logistics regression model that can help predict if an employee will churn or not.
Domain: Workforce Analytics
Project 5: NYC 311 Service Request Analysis
Perform a service request data analysis of New York City 311 calls. You will focus on data wrangling techniques to understand patterns in the data and visualize the major complaint types.
Domain: Telecommunication
Project 6: MovieLens Dataset Analysis
The GroupLens Research Project is a research group in the Department of Computer Science and Engineering at the University of Minnesota. The researchers of this group are involved in several research projects in the fields of information filtering, collaborative filtering, and recommender systems. Here, we ask you to perform an analysis using the exploratory data analysis (EDA) technique for user datasets.
Domain: Engineering
Project 7: Stock Market Data Analysis
As a part of this project, you will import data using Yahoo DataReader from the following companies: Yahoo, Apple, Amazon, Microsoft, and Google. You will perform fundamental analytics, including plotting, closing price, plotting stock trade by volume, performing daily return analysis, and using pair plot to show the correlation between all of the stocks.
Domain: Stock Market
Project 8: Titanic Dataset Analysis
On April 15, 1912, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This tragedy shocked the world and led to better safety regulations for ships. Here, we ask you to perform an analysis using the EDA technique, in particular applying machine learning tools to predict which passengers survived the tragedy.
