Introduction to Data Science in Python

This course introduces the basics of Python 3, including conditional execution and iteration as control structures, and strings and lists as data structures. Course outline and certification by University of Michigan

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Key Points About This Course​

Duration: 3 Days
Time: 9.00am-5.00pm
Public Class Fee: RM 3,500.00
Virtual Class Fee: RM 2,975.00
HRDF Claimable

Course Overview

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

What You Will Learn

  • Describe common Python functionality and features used for data science
  • Explain distributions, sampling, and t-tests
  • Query DataFrame structures for cleaning and processing
  • Understand techniques such as lambdas and manipulating csv files

Skills You Will Gain

  • Python Programming
  • Numpy
  • Pandas
  • Data Cleansing

Course Content

Part 1

In this part you’ll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus.

  • Data Science
  • The Coursera Jupyter Notebook System
  • Python Functions
  • Python Types and Sequences
  • Python More on Strings
  • Python Demonstration: Reading and Writing CSV files
  • Python Dates and Times
  • Advanced Python Objects, map()
  • Advanced Python Lambda and List Comprehensions
  • Advanced Python Demonstration: The Numerical Python Library (NumPy)

Part 2

In this part of the course you’ll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing — pandas. You’ll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed.

  • Introduction
  • The Series Data Structure
  • Querying a Series
  • The DataFrame Data Structure
  • DataFrame Indexing and Loading
  • Querying a DataFrame
  • Indexing Dataframes
  • Missing Values

Part 3

In this part you’ll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We’ll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis.

  • Merging Dataframes
  • Pandas Idioms
  • Group by
  • Scales
  • Pivot Tables
  • Date Functionality

Part 4

In this part of the course you’ll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests.

  • Introduction
  • Distributions
  • More Distributions
  • Hypothesis Testing in Python

Training Schedule

1 – 3 Feb 2021
19 – 21 Apr 2021
14 – 16 Jun 2021
2 – 4 Aug 2021
20 – 22 Oct 2021
20 – 22 Dec 2021

  • Public Class Training

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  • Examination (Optional)

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