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Big Data Analytics

Introduction to Python with Data Analytics

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  • Overview

    This is a comprehensive introduction to Python programming with a focus on understanding and using the Pandas library for storing data in DataFrames and plotting portions of the data with matplotlib. In addition to data visualization, you will learn how to use the Pandas library to import and filter data. Typical data science skills such as data interpretation and analysis will be addressed.

    Topics covered include:

    • Python basics including types, control flow, collections (lists, dictionaries), functions, and Python classes
    • NumPy arrays
    • Creating, filtering, and cleaning data with Pandas DataFrames
    • Importing CSV data
    • Visualization with matplotlib
    • Jupyter notebooks
  • Who Should Take This Course


    This course is suitable for: Data analysts, Data scientists, Data engineers, and Developers.


    Students should have basic proficiency in some programming language, experience with Excel, and/or CSV files. Students should have strong analytical and problem-solving skills and an understanding of basic analytical concept such as data import and graphing data.

  • Why You Should Take This Course

    At the end of the course students will be able to:

    • Write Python programs at a beginner to intermediate level
    • Use Python to import CSV files into a DataFrame
    • Manipulate data in DataFrames using the Pandas library
    • Create basic plots for data visualization using matplotlib
  • Schedule
  • Course Outline


    • RDBMS Basics and Concepts
    • SQL Basics and Review with PostgreSQL
    • Logical operators and Arithmetic operators
    • Data cleaning and scrubbing with SQL
    • SQL Functions
    • Importing and Exporting Data

    Data Analysis with Excel

    • Basic spreadsheet operations
    • Importing Data: CSV, Fixed width text
    • Absolute vs. relative references
    • Functions
    • Lookups: VLOOKUP, HLOOKUP
    • Data Filtering
    • Pivot Tables
    • Excel Chart Basics

    Data Analysis with Python

    • Python Basics
    • Overview of Python Packages for Data Analysis
    • NumPy, Pandas, SciPy, SciKit-Learn
    • Overview of Python Packages for Visualization
    • Matplotlib, Seaborn
    • Reading Excel data into Pandas
    • Exploring Data Frames
    • Data Frame data types
    • Data Frame attributes
    • Selecting Data
    • Slicing Data
    • Filtering Data
    • Sorting Data
    • Grouping Data
    • Aggregate Functions and Descriptive Statistics
    • Plotting Data and Graphs

    Putting it Together with PowerPoint

    • Graphing in PowerPoint
    • Adding graphs from other sources
  • FAQs
    Is there a discount available for current students?

    UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Online courses are excluded from this offer.

    What is the cancellation and refund policy?

    Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.

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