Data Analysis Using Python

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3 reviews

Course Description

Why Data Analysis

The outburst of data is transforming businesses. Companies – big or small – are now expecting their business decisions to be based on data-led insight. Data specialists have a tremendous impact on business strategies and marketing tactics. As of 2022, 52% of business worldwide consider data analytics and predictive analytics primary parts of their operations.

Welcome to Data Analysis with Python 🙂

This Learning path is great for beginners and intermediate levels alike, as it starts with the fundamentals and works through advanced topics. Completing this path will set you up for success as a competent data analyst. In this course you learn to handle some of the most common dirty data problems. You will learn how to perform summary statistics on DataFrame, visualize the contents of your data, mitigating missing data values, explore different techniques for merging, joins and preparing your data for predicting and better decision making.

What’s in this course?

This course is focused on how to manipulate your data using Python. In this course you will learn about:

  • What is Data Analysis
  • Setting up your notebook
  • Introduction to Pandas
  • Data manipulation with pandas
  • Merging Data in Python (Data Wrangling)
  • Data Visualization (Matplotlib, Seaborn)
  • Data Cleaning and Preparation
  • Exploratory Data Analysis
  • Probability and Statistical Thinking
  • Handling datetime for Time Series Analysis

Are there any course requirements or prerequisites?

This is a beginner friendly course so anyone can start with this immediately, however it’s expected for the students to have some basic knowledge about Python syntax

What you’ll learn

Data Manipulation with pandas

  • We’ll master the pandas basics. Learn how to inspect DataFrames and perform fundamental manipulations, including sorting rows, subsetting, and adding new columns.
  • You’ll calculate summary statistics on DataFrame columns, and master grouped summary statistics and pivot tables.
  • Indexes are supercharged row and column names. Learn how they can be combined with slicing for powerful DataFrame subsetting.
  • Learn to visualize the contents of your DataFrames, handle missing data values, and import data from and export data to CSV files.

Merging DataFrames with pandas

  • You’ll learn about different techniques you can use to import multiple files into DataFrames. Having imported your data into individual DataFrames, you’ll then learn how to share information between DataFrames using their indexes.
  • You’ll learn about appending and concatenating DataFrames
  • You’ll explore different techniques for merging, and learn about left joins, right joins, inner joins, and outer joins, as well as when to use which. You’ll also learn about ordered merging, which is useful when you want to merge DataFrames with columns that have natural orderings, like date-time columns.

Cleaning Data in Python

  • You’ll learn how to overcome some of the most common dirty data problems. You’ll convert data types, remove duplicate values and understand what are the approaches to take before making that decision.
  • You’ll learn how to use regular expressions, parsing dates and where and how to use the date-time format.
  • Advanced data cleaning problems, removing and imputation missing data points and using some of the powerful libraries to visualize the missing data.

Exploratory Data Analysis in Python

  • The first step of almost any data project is to read the data, check for errors and special cases, and prepare data for analysis.
  • You’ll learn how to represent distributions using histograms, how to identify what attributes the current data holds.
  • You’ll explore relationships between variables two at a time, using scatter plots and other visualizations to extract insights from a new dataset

Probability

The Basic Probability Formula, Computing Expected Values, Frequency, Events and Their Complements. Sets and Events, union and Mutually Exclusive sets, Dependence and Independence of Sets, The Conditional Probability Formula, The Additive Rule, multiplication law, Bayes’ Law. Probability Distributions; Conditional probability, what’s the use of law of total probability and how this compliments the Bayes’ Law.

Who is this course for?

This course is designed for two main types of audiences:

  • If you’re someone looking to start your career in Data Analysis, upgrading your skills or simply for stepping into the freelancing market.
  • If you’re a business owner, serial entrepreneur or someone who wants to extract meaningful insights for marketing, gaining more traffics or for simple dealings of your business. All the while making sure your tech team can adopt the approach and build over it in future.

If you fall in one for the two audiences mentioned above, then my friend you’ve stumble upon the right place

Attached file

File size: 70 kb

Curriculum

Introduction to Data Analysis

1
Instructor and Course Introduction
1 Min
2
M1 L1 What is Data Analysis
5 Mins
3
M1 L2 Introduction to Notebooks
1 Min
4
M1 L3 Google Colab Overview
3 Mins
5
Quiz
2 questions

Introduction to Pandas

1
M2 L1 Inspecting DataFrame
11 Mins
2
M2 L2 Some Basic Methods
5 Mins
3
M2 L3 Sub Setting Columns
8 Mins
4
M2 L4 Summary Statistics
3 Mins
5
Quiz
2 questions

Data Manipulation with Pandas

1
M3 L1 Selection with loc and iLoc
10 Mins
2
M3 L2 Slicing and Indexing
10 Mins
3
M3 L3 Reshaping Data
5 Mins
4
Quiz
1 question

Merging Dataframes with Pandas

1
M4 L1 Indexing and Reindexing
10 Mins
2
M4 L2 Concatenating Series
5 Mins
3
M4 L3 Appending Through Axis
8 Mins
4
M4 L4 Joining Techniques
10 Mins
5
M4 L5 Merging Dataframes
15 Mins
6
Quiz
2 questions

Data Visualization

1
M5 L1 Getting started with Matplotlib
8 Mins
2
M5 L2 Matplotlib Subplots
3 Mins
3
M5 L3 Matplotlib Interface
10 Mins
4
M5 L4 Getting started with Seaborn
4 Mins
5
M5 L5 Seaborn Subplots
2 Mins
6
M5 L6 Line Plots
4 Mins
7
M5 L7 Bar Plots
3 Mins
8
M5 L8 Scatter Plots
4 Mins
9
M5 L9 Histograms
3 Mins
10
Quiz
1 question

Data Cleaning and Preparation

1
M6 L1 Handling Missing Data
14 Mins
2
M6 L2 Visualizing Missing Data
13 Mins
3
M6 L3 Removing Missing Data
12 Mins
4
M6 L4 Data Imputation
5 Mins
5
M6 L5 Removing Duplicate Values
6 Mins
6
M6 L6 Parsing Dates
10 Mins
7
M6 L7 Regular Expressions
5 minutes
8
M6 L8 Type Conversions
4 mins
9
Quiz
2 questions

Introduction to Probability

1
M7 L1 Sets and Events
10 Mins
2
M7 L2 Exclusive/Non-Mutually Exclusive Events
15 Mins
3
M7 L3 Independent/Dependent Events
10 Mins
4
M7 L4 Laws of Probability
4 Mins
5
M7 L5 Conditional Probability: Practice Questions 01
6 Mins
6
M7 L6 Conditional Probability: Practice Questions 02
10 Mins
7
M7 L7 Law of Total Probability
9 Mins
8
M7 L8 Bayes Theorem
10 Mins
9
Course Conclusion
1 Min
10
Quiz
2 questions

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