Introduction
After statistics essentials, this course acquaints students to data analysis with the Python (most popular data science language today). You learn to work with different data structures in Python using the most popular data analytics and visualization packages such as numpy, pandas, matplotlib, and seaborn. Ultimately, students will use Python code and packages to solve problems; extract, transform, load, and analyze data to gain insights; and communicate the analyses, aided by appropriate visualizations. The course targets students mainly beginners who want to learn the basics of data analysis with python, or intermediate learners who want to improve their skills and apply them to real-world problems.
Objectives and Outcome
The objectives of this course are to help you learn how to collect, clean, manipulate, analyze, and visualize data using python, and to use various python libraries and tools for data science, such as pandas, numpy, matplotlib, scikit-learn, and more.
The Prerequisites
To be successful in this course, basic programming knowledge is necessary. However, this course do not assume any previous knowledge in python programming, such as data types, variables, operators, functions, loops, and conditions, and to have a basic understanding of data analysis concepts, such as statistics, probability, and machine learning.
Detailed Course Outline
Introduction to data analysis with python
• What is data analysis and why use python?
• Setting up the python environment and tools
• Importing and exporting data with python
Data manipulation with pandas
• What is pandas and how to use it?
• Creating and exploring data frames
• Filtering, sorting, and grouping data
• Merging, joining, and concatenating data
• Putting it all together with real world data/Portfolio
Data visualization with matplotlib
• What is matplotlib and how to use it?
• Creating and customizing plots
• Choosing the right plot for your data
• Adding labels, legends, and annotations
Putting it all together with real world data/Portfolio
Data analysis with numpy
• What is numpy and how to use it?
• Creating and manipulating arrays
• Performing arithmetic and logical operations
• Applying statistical and mathematical functions
Machine Learning
• Concepts in Machine learning
• Data analysis with scikit-learn
• What is scikit-learn and how to use it?
• Preprocessing and transforming data
• Splitting and cross-validating data
• Evaluating and comparing models
2. Introduction
After statistics essentials, this course
acquaints students to data analysis with
the Python
You learn to work with different data
structures in Python using the most
popular data analytics and visualization
packages such as numpy, pandas,
matplotlib, and seaborn
The course targets students mainly
beginners who want to learn the basics of
data analysis with python, or intermediate
learners who want to improve their skills
and apply them to real-world problems
3. Objectives and
Outcome
+ The objectives of this course
are to help you learn how to
collect, clean, manipulate,
analyze, and visualize data
using python, and to use
various python libraries and
tools for data science, such as
pandas, numpy, matplotlib,
scikit-learn, and more
4. The Prerequisites
+ To be successful in this course, basic
programming knowledge is necessary
+ However, this course do not assume
any previous knowledge in python
programming, such as data types,
variables, operators, functions, loops,
and conditions, and to have a basic
understanding of data analysis
concepts, such as statistics,
probability, and machine learning
6. Introduction to
data analysis
with python
What is data analysis and
why use python?
Setting up the python
environment and tools
Importing and exporting
data with python
7. Data Manipulation
with Pandas
+ What is pandas and how to use it?
+ Creating and exploring data frames
+ Filtering, sorting, and grouping data
+ Merging, joining, and concatenating
data
+ Putting it all together with real world
data/Portfolio
8. Data
Visualization
with Matplotlib
What is matplotlib and how to use
it?
Creating and customizing plots
Choosing the right plot for your data
Adding labels, legends, and
annotations
10. Data Analysis
with Numpy
What is numpy and how to use it?
Creating and manipulating arrays
Performing arithmetic and logical
operations
Applying statistical and
mathematical functions
11. Machine Learning
+ Concepts in Machine learning
+ Data analysis with scikit-learn
+ What is scikit-learn and how to use
it?
+ Preprocessing and transforming
data
+ Splitting and cross-validating data
+ Evaluating and comparing models
