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Using Python for Data Analytics

Using Python for Data Analytics

$1,195.00 Per Enrollment

Price Includes:

16 hours instructor-led training, Courseware and ‘Price & Quality Guarantee’

The ability to analyze, clean and visualize business data has become a critical element of the decision making process within modern organizations. Increasingly, the Python programming language is being used to manipulate and understand data, using its range of robust data science libraries.

Aimed at IT professionals with an understanding of data science principles and experience using tools such as Microsoft Excel and SQL, our 2 day class will teach you how to load, manipulate, understand and present complex business data.

Using Python for Data Analytics is an instructor-led class which can be taken in-person, or live-online. 



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    Get your Using Python for Data Analytics training in our convenient IT training centers in Maryland or Virginia.

    Why Take the Using Python for Data Analytics Class?

    The ability to mine, analyze, and present complex data has become a critical element of modern business operations. Organizations are increasingly relying on the collection and augmentation of data sets to drive strategy and inform business decisions. This is particularly true in the Washington DC region, where many large government agencies and corporations are headquartered.

    Over the last several years the Python programming language has emerged as the tool of choice for serious data analysts. With a range of robust data manipulation and presentation libraries, Python enables developers and analysts to create data tools that are far more complex and customized to a businesses needs than typical analysis tools such as Microsoft Excel. 

    The TrainACE Using Python for Data Analytics class is a two day, instructor-led course, that will teach you how to use Pythons powerful libraries to load, manipulate, clean and present data.

    You'll learn how to:

    • Implement the Python data science environment
    • Use NumPy arrays to analyze and manage datasets
    • Use NumPy arrays to modify and manipulate datasets
    • Utilize pandas DataFrames to analyze and manage datasets
    • Manipulate data using pandas DataFrames, as well as modify and present it.
    • Use Matplotlib and Seaborn to visualize datasets

      Why Learn Python Data Science tools?

      If you're a developer, or you perform a data science role, the ability to build programs that manipulate and present data clearly and easily is a powerful skill. These days nearly all organizations rely heavily on data analysis to make important business decisions, but they lack people with the skills to quickly generate and present analysis. Learning the data analytics libraries and how to use them can only enhance your opportunities for your current or future role.

      Why Take a Python Data Science Tools Based Course?

      Over the years Microsoft Excel and similar tools have become great out-of-the-box data analytics solutions, but the volume and complexity of today's data means that organizations often need a much more customized solution. Handling huge datasets in unique ways suited to specific businesses requires tools that can be shaped to the organizations needs.

      The Python programming language offers a powerful and easy to use solution for large organizations with complex datasets. Python therefore provides the ability to build complex and customized apps that are tailored to specific organizations.

      IT professionals, and data scientists with the skills to program in Python using it's data analysis libraries are very much in-demand throughout Washington DC, Maryland and Virginia.

      It’s worth noting that programmers in the Washington DC region earn salaries that are as much as 16% higher than the national average. Learning to program in Python using data analysis tools will set you up to have a higher earning potential, especially if you work in the metro DC area.


      How to Learn Coding Data Analytics Tools in Python

      If you're looking to learn all about Python's data science tools, our comprehensive, practical and hands-on training course is ideal. We've helped thousands of students in the Washington, DC region, Maryland and Virginia attain roles in all types of IT, data science, and cybersecurity roles – including those that require an understanding of how to use Python as a data analysis and visualization tool.

      We are committed to providing students with the very best course instructors and learning environments. We believe in our training so much that we offer a price and quality guarantee for all our classes. Register for the Python Programming class today to start your training.


      Audience and Prerequisites:

      This Using Python for Data Analytics class is ideal for developers or data scientists who are looking to expand their ability to mine, analyze, and visualize knowledge from business data. If you are an experienced data scientist or developer, looking for ways to manipulate data in unique and customized ways, beyond the functionality of standard spreadsheet and database applications, this is a great class to take.

      Candidates for this class should have a sound understanding of fundamental data science concepts. You should have experience using standard data tools such as Microsoft Excel and SQL, and be proficient in using the Python programming language.

      If you are unfamiliar with Python or need a refresher, take a look at our Python Programming course,


      What will I learn in this Using Python for Data Analytics class?

      Topics & Concepts Covered in Using Python for Data Analytics Include:

      1. Setting Up a Python Data Science Environment Topic

      • Selecting Python Data Science Tools
      • Installing Python Using Anaconda Topic
      • Setting Up an Environment Using Jupyter Notebook

      2. Managing and Analyzing Data with NumPy Topic

      • Creating NumPy Arrays
      • Loading and Saving NumPy Data
      • Analyzing Data in NumPy Arrays

      3. Transforming Data with NumPy

      • Manipulating Data in NumPy Arrays
      • Modify Data in NumPy Arrays

      4. Managing and Analyzing Data with pandas

      • Creating Series and DataFrames
      • Load and Save pandas Data
      • Analyze Data in DataFrames
      • Slice and Filter Data in DataFrames

      5. Transforming and Visualizing Data with pandas

      • Manipulate Data in DataFrames
      • Modify Data in DataFrames
      • Plot DataFrame Data Lesson

      6. Visualizing Data with Matplotlib and Seaborn

      • Create and Save Simple Line Plots
      • Create Subplots
      • Create Common Types of Plots
      • Format Plots
      • Streamline Plotting with Seaborn