# Data Science with Python E-learning

## Kuvaus

#### ABOUT THE COURSE

The Python for Data Science course covers the fundamental concepts of programming with Python and explains data analytics, machine learning, data visualisation, web scraping and natural language processing. You will gain a comprehensive understanding of the various packages and libraries required to perform the data analysis aspects.

#### DURATION

Approximately 12 hours.

#### PRE-REQUISITES

There are no prerequisites for this data science course and the Python basics course that is included with this programme provides you with additional coding guidance.

#### LEARNING OBJECTIVES

By the end of the course you will be able to:

- Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualisation, hypothesis building, and testing
- Install the required Python environment and other auxiliary tools and libraries
- Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
- Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
- Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimise, Statistics, IO and Weave
- Execute data analysis and manipulation using data structures and tools provided in the Pandas package
- Gain expertise in machine learning using the Scikit-Learn package
- Understand supervised 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
- Use the matplotlib library of Python for data visualisation
- Extract useful data from websites by performing web scrapping using Python
- Integrate Python with Hadoop, Spark and MapReduce

- 12 months online access to the Data Science for Python e-learning
- Four real-life industry-based projects
- Interactive learning with Jupyter notebooks labs

This course is offered by Simplilearn, a partner of ILX Group.

#### WHAT'S COVERED?

The course covers the following topics:

- Lesson 1 - Data science overview
- Lesson 2 - Data analytics overview
- Lesson 3 - Statistical analysis and business applications
- Lesson 4 - Python environment setup and essentials
- Lesson 5 - Mathematical computing with Python (NumPy)
- Lesson 6 - Scientific computing with Python (Scipy)
- Lesson 7 - Data manipulation with Pandas
- Lesson 8 - Machine learning with Scikit–Learn
- Lesson 9 - Natural language processing with Scikit Learn
- Lesson 10 - Data visualisation in Python using matplotlib
- Lesson 11 - Web scraping with BeautifulSoup
- Lesson 12 - Python integration with Hadoop MapReduce and Spark
- FREE COURSE - Maths refresher
- FREE COURSE - Statistics essentials for data science

The course also includes 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 on other 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 like CPI, unemployment rate, etc. have an impact on sales.

Domain: Retail

- Project 3: Improving customer experience for Comcast

Comcast, one of the US-based global telecommunication companies wants to improve customer experience by identifying and acting on problem areas that lower customer satisfaction if any. 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 visualise 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 technique for user datasets.

Domain: Engineering

- Project 7: Stock market data analysis

As a part of this project, you will import data using Yahoo data reader 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 exploratory data analysis technique, in particular applying machine learning tools to predict which passengers survived the tragedy.

#### TARGET AUDIENCE

The Python for Data Science training course is recommended for anyone with a genuine interest in the data science field, including:

- Software professionals looking to get into analytics
- Analytics professionals who are interested in working with Python
- IT professionals or graduates interested in pursuing a career in analytics and data science
- Experienced professionals who would like to apply data science skills in their fields

To become a certified, you must fulfill the following criteria:

- Complete one project out of the two provided in the course. Submit the deliverables of the project in the LMS which will be evaluated by the lead trainer
- Score a minimum of 60% in any one of the two simulation tests
- Complete the course