(All course fees are in USD)

Course Description

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.

Co-Developed by

IBM & Simpliearn

Offered in Partnership with

Simplilearn

Course Delivery

• Online pre-recorded self-paced learning (24 hours)
• Live virtual classroom training held at fixed schedule, as determined at our discretion (44 hours).

Total online blended learning: 68 hours

Benefits

• Data Science is an evolving field and Python has become a required skill for significant portion of jobs in Data Science.
• 68 hours of blended learning (24 hours online self-paced learning + 44 hours of instructor-led online training)
• 4 industry-based projects
• Interactive learning with Jupyter notebooks labs
• Dedicated mentoring session from faculty of industry experts

Co-developed by

IBM & Simplilearn

Award upon Successful Completion

Data Science with Python Certificate of Achievement from Simplilearn

Awarding Organisation

Simplilearn

Learning Outcomes

• 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 datatypes, 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

Assessments

A score of at least 75% in course-end assessment, and below:

Online Classroom

• Attend one complete batch of Python for Data Science training.
• Submit at least one completed project, with satisfactory evaluation by instructor.

Online Self-Learning

• Complete 85% of the course
• Submit at least one completed project, with satisfactory evaluation by instructor.

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 nonrated 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

Domain: Hazard

Who Should Enrol

• Analytics professionals willing to work with Python
• Software and IT professionals interested in analytics
• Anyone with a genuine interest in data science

Prerequisites

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.

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

Course Overview

Lesson 00 – Course Overview

Lesson 01 – Data Science Overview

Lesson 02 – Data Analytics Overview

Lesson 03 – Data Analytics Overview

Lesson 04 – Python Environment Setup and Essentials

Lesson 05 – Mathematical Computing with Python (NumPy)

Lesson 06 – Scientific computing with Python (Scipy)

Lesson 07 – Data Manipulation with Pandas

Lesson 08 – Machine Learning with Scikit–Learn

Lesson 09 – Natural Language Processing with Scikit Learn

Lesson 10 – Data Visualization in Python using matplotlib

Lesson 11 – Web Scraping with BeautifulSoup

Lesson 12 – Python integration with Hadoop MapReduce and
Spark

Accessible Period of Course

1 Year from date of enrolment

Customer Reviews

C Muthu Raman

Technical Project Leader – Mahindra Truck & Bus
Simplilearn facilitates a brilliant platform to acquire new and
relevant skills with ease. Well laid-out course content and expert
faculty ensure an excellent learning experience.

Mukesh Pandey

Technical Lead|Python|MS SQL Server|SSIS|Power BI|T-SQL
Simplilearn is an excellent platform for online learning. Their course
curriculum is comprehensive and up to date. We get lifetime
access to the recorded sessions in case we need to refresh our
understanding. If you are looking to upskill, I suggest you sign up
with Simplilearn. They offer classes in almost all disciplines.

Mukesh Pandey

Sr. Software engineer at Coupa Software
Incredible mentorship and amazing, unique lectures. Simplilearn
provides a great way to learn with self-paced videos and recordings
of online sessions. Thanks, Simplilearn, for providing quality
education.

Surendaran Baskaran

I took the Data Science with Python course with Simplilearn. The
instructor is knowledgeable and shares their skills and knowledge.
My learning experience has been outstanding with Simplilearn. The
practice labs and materials are helpful for better learning. Thank you,
Simplilearn!