Non-Certification Course

Introduction to Data Science in Python

This course covers the basics of the Python programming environment and data manipulation using the Pandas library. It introduces fundamental techniques such as lambdas and numpy and teaches data cleaning, manipulation and basic statistical analysis. By the end, students will be able to work with tabular data and run inferential analyses. It is recommended to take this course before other Applied Data Science with Python courses.

RM 4,799.00

per person

Level

Foundation

Duration

4 Days

Training Delivery Format

Face-to-face / Virtual Class

RM 4,799.00

per person

Level

Foundation

Duration

4 Days

Training Delivery Format

Face-to-face (F2F) / Virtual Class

Class types

Public Class

Private Class

In-House Training

Bespoke

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.

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.

For those who are just starting out in programming.

By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

Course Objectives

What You Will Learn

  • Python Programming
  • Numpy
  • Pandas
  • Data Cleansing

Ideal if you have experience with basic Python

Module 1: Fundamentals of Data Manipulation with Python

In this module 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)

Module 2: Basic Data Processing with Pandas

In this module 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

Module 3: More Data Processing with Pandas

In this module 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

Module 4: Answering Questions with Messy Data

In this module 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

 

This is a non-certification course.

If you are looking for a certification, Pyhton Institute offers the best options for you to choose from.

You can start with PCEP, Certified Entry Level Python Programmer.

Then you can pursue PCAP, Certified Associate in Python Programming, and then PCPP, Certified Professional in Python Programming 1.

 

Q: What is the main focus of the course “Introduction to Data Science in Python”?

A: The main focus of the course is to provide an introduction to the field of data science using Python as the primary programming language. It covers fundamental data manipulation techniques, data cleaning and processing using pandas, and statistical techniques for analyzing messy data.

 

Q: What will I learn about Python in this data science course?

A: In this course, you will learn about various Python functionalities and features that are commonly used in data science tasks. This includes working with Python functions, handling different types of data and sequences, processing strings, reading and writing CSV files, dealing with dates and times, and utilizing advanced Python libraries like NumPy.

 

Q: How will the course help me in data cleaning and processing?

A: The course will equip you with the knowledge of pandas, one of the most essential toolkits for data cleaning and processing in Python. You will learn how to read data into DataFrame structures, query and manipulate the data, handle missing values, merge DataFrames, group data, and create pivot tables for summary analysis.

 

Q: Will I gain insights into working with messy data in this course? A:

Yes, the course covers techniques to deal with messy data. You will be introduced to various statistical concepts such as distributions, sampling, and hypothesis testing using t-tests. These techniques will enable you to analyze and draw meaningful conclusions from messy datasets.

 

Q: Are there any prerequisites for taking this Introduction to Data Science course?

A: The course assumes basic familiarity with Python programming. It is beneficial to have prior knowledge of Python functions, data types, and handling files. However, there are no specific prerequisites in terms of data science or programming background. Beginners with a basic understanding of Python can also enroll and benefit from the course.

HRD Corp Claimable Course

At this time, this course is available for private class and in-house training only. Please contact us for any inquiries. 

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