Python for Data Science Certification Training Course
The Data Science with Python course is designed to impart an in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using Python. The course is packed with real-life projects, assignment, demos, and case studies to give a hands-on and practical experience to the participants.
Mastering Python and using its packages: The course covers PROC SQL, SAS Macros, and various statistical procedures like PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP. You will learn how to use SAS for data exploration and data optimization.
Mastering advanced analytics techniques: The course also covers advanced analytics techniques like clustering, decision tree, and regression. The course covers time series, it’s modeling, and implementation using SAS.
As a part of the course, you are provided with 4 real-life industry projects on customer segmentation, macro calls, attrition analysis, and retail analysis.
- Gain an in-depth understanding of data science process, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
- Install the required Python environment and other auxiliary tools and libraries
- Understand the essential concepts of Python programming like data types, tuples, lists, dicts, basic operators, and functions.
- Perform high-level mathematical computing using NumPy package and its large library of mathematical functions
- Perform scientific and technical computing using SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave.
- Perform data analysis and manipulation using data structures and tools provided in Pandas package
- Gain expertise in machine learning using the Scikit-Learn package
- Gain an in-depth understanding of supervised learning and unsupervised learning models like linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline
- Use Scikit-Learn package for natural language processing
- Use matplotlib library of Python for data visualization
- Extract useful data from websites by performing web scrapping using Python
- Integrate Python with Hadoop, Spark, and MapReduce
- Analytics professionals who want to work with Python
- Software professionals looking for a career switch in the field of analytics
- IT professionals interested in pursuing a career in analytics
- Graduates looking to build a career in Analytics and Data Science
- Experienced professionals who would like to harness data science in their fields
- Anyone with a genuine interest in the field of Data Science
The course includes four real-life, industry-based projects. Successful evaluation of one of the following projects is a part of the certification eligibility criteria:
Project-1: NYC 311 Service Request Analysis
Telecommunication: Perform a service request data analysis of New York City 311 calls. You will focus on the data wrangling techniques to understand the pattern in the data and also visualize the major complaint types.
Project-2: MovieLens Dataset Analysis
Engineering: 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 many research projects related to the fields of information filtering, collaborative filtering, and recommender systems. Here, we ask you to perform the analysis using the Exploratory Data Analysis technique for user datasets.
Project-3: Stock Market Data Analysis
Stock Market: As a part of the project, you need to import data using Yahoo data reader of the following companies: Yahoo, Apple, Amazon, Microsoft, and Google. 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 the stocks.
Project-4: Titanic Dataset Analysis
Hazard: 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 the analysis through the exploratory data analysis technique. In particular, we want you to apply the tools of machine learning to predict which passengers survived the tragedy.
- Lectures 13
- Quizzes 0
- Students 0
- Assessments Self