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Big Data Analytics

Data Analysis with Python, SQL and Excel

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  • Overview

    This course takes a practical approach to understanding key methods for Data Analytics by using common tools: SQL, Excel, and Python. Participants will perform common analytics activities: Interacting with a SQL database; writing an ETL script; outputting to Excel; use Excel and Tableau for data visualization.

  • Who Should Take This Course


    Participants should have a basic knowledge of SQL and an understanding of programming.

  • Why You Should Take This Course

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

    • Program SQL at a basic to intermediate skill level
    • Program Python at a basic to intermediate skill level
    • Create appropriate SQL queries to gather necessary data from relational databases and other data sources
    • Import data sources into Excel and Python
    • Create data visualizations in Excel
    • Use the NumPy package to work with arrays
    • Use the Pandas to create and structure data with data frames
    • Work with the Jupyter Notebook Environment for Python Analysis
    • Create data visualizations with matplotlib and Seaborn packages
    • Make inferences from data and visualizations
    • Incorporate data visualizations into PowerPoint
  • 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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