1-Day Python Data Analytics: With Pandas, Numpy, And Matplotlib
Your Complete Guide to doing Data Analysis with the Python Pandas Library
Learn to Acquire Data with NumPy and Pandas, Transform it with Pandas, and Visualize it with Matplotlib
The world generates data at an increasing pace. Consumers, sensors, or scientific experiments emit data points every day. In finance, business, administration and the natural or social sciences, working with data can make up a significant part of the job. Being able to efficiently work with small or large datasets has become a valuable skill. Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis.
Learn how to usePython for data processing, management, and information retrieval. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics is an critical skill set to help you in storing, accessing and analyzing data.
Learn & Add this In-Demand Skills to Your Skills Set
This is the most comprehensive, yet straight-forward Data Analytics Using Python Course. Whether you have never worked with Data Science before, already know basics of Python, or want to learn the advanced features of Plotly and Pandas with Python 3, this course is for you! In this course, you will learn how to use the popular Python Pandas data science library for data analysis. It will teach you useful data manipulation and cleaning techniques including import/export of data, Series and DataFrame data types, and how to use functions such as groupby, merge, and pivot tables for data aggregation.
- Understand the data analysis ecosystem in Python.
- How to Perform data analysis with python using the pandas library.
- Learn how to use the pandas data analysis library to analyze data sets
- Hands-on Analyze real datasets to better understand techniques for data analysis
- What is Numpy and Arrays?
- Understand some of the basic concepts of data analysis using Panda including Selecting, Slicing, Filtering and Dealing with Missing or Duplicated Data
- Have used n-dimensional arrays in NumPy as well as the pandas Series and DataFrames to analyze data.
- Create how to create basic plots of data using MatPlotLib including Plot Types, Attributes, Styling
- Using Matplotlib for Data Visualization
Who Should Attend?
- Suited for Data analyst Finance, Business and Marketing Analyst seeking to gain deeper understanding of using Python for data analysis.
- Hardware, Systems, IoT Engineers, Systems Administrator
- Managers, Engineers, Designers, Accountants, Lawyers, Healthcare Professionals
- Other professionals from many backgrounds engaged in Data Analytics
Note: Participant is required to bring their own laptop with access to internet (WiFi network will be provided)
Data Scientist: Dr Sudipta Samanta, Senior Research Engineer at Temasek Life Sciences Lab
Dr. Sudipta Samanta has over 15 years of research and teaching experience. Dr Sudipta received his Ph.D. for his research in the field of computational Biophysics and has over 10 years of research experience as a Research Scientist. His current interests include Machine Learning, Deep Learning, Health Care Data analysis and computer simulation. Prior to joining Temasek, Dr Sudipta was Visiting Scientist of Internationally renowned MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), USA from 2011 to 2018 and a Research Scientist at Singapore-MIT Alliance for Research & Technology.
Topic 1 Different types of Data
• Basic steps in Data Analysis
• Python Data Analysis Libraries
Topic 2 Data Analysis with Pandas
• What is Pandas?
• Data Structures in Pandas
• Important Non-Mathematical Functions (E.g. head, tail, etc.)
• Selecting Columns and Rows
• Data Manipulation with Pandas
• Importing and Exporting Data
• Graphics in Pandas
Topic 3 Numpy
• Overview of Numpy
• Basic operations
• Creation of Numpy Array
• Array Operation
• Indexing and Slicing Array Elements
• Filtering Array Elements
• Transforming Array
• Statistical Operations
• Random Number Generation
Topic 4 Plot graph and Data Visualization
• Overview of Matplotlib?
• Figures and subplots
• Colors, Markers, and Line Styles
• Saving plots
• Different plotting functions
• Line plots, Bar plots, Scatter plots, etc.
• Histograms and Density plots
Topic 5: Different Data types and data pre-processing for Model building
• Tabular Data
• Image Data
• Sequential Data