Duration
2 days
What you'll learn
- Understanding the basic statistic and how you can use it for different machine learning scenarios
- Become Proficient In Using The Most Common Python Data Science Packages Including Numpy, Pandas, Scikit & Matplotlib
- Be Able To Read In Data From Different Sources (Including Webpage Data) & Clean The Data
- Carry Out Data Exploratory & Pre-processing Tasks Such As Tabulation, Pivoting & Data Summarizing In Python
- Become Proficient In Working With Real Life Data Collected From Different Sources
- Carry Out Data Visualization & Understand Which Techniques To Apply When
- Carry Out The Most Common Statistical Data Analysis Techniques In Python Including T-Tests & Linear Regression
- Understand The Difference Between Machine Learning & Statistical Data Analysis
Who this course is for
- Anyone Who Wishes To Learn Practical Data Science Using Python
- People Looking To Work With Real Life Data In Python
- Anyone With A Prior Knowledge Of Python Looking To Branch Out Into Data Analysis
- Anyone Looking To Become Proficient In Exploratory Data Analysis, Statistical Modelling & Visualizations Using iPython
Course Outline
1- Introduction to the Data Science in Python
- What is Data Science?
- Introduction to the Python Data Science Tool
- Introduction to the Python Data Science Environment
2- Introduction to Python Pre-Requisites for Data Science
- Different Types of Data Used in Statistical & ML Analysis
- Different Types of Data Used Programatically
- Python Data Science Packages To Be Used
3- Introduction to Numpy
- Numpy: Introduction
- Create Numpy Arrays
- Numpy Operations
- Matrix Arithmetic and Linear Systems
- Numpy for Basic Vector Arithmetric
- Numpy for Basic Matrix Arithmetic
- Broadcasting with Numpy
- Solve Equations with Numpy
- Numpy for Statistical Operation
4- Introduction to Pandas
- Data Structures in Python
- Read in Data
- Read in CSV Data Using Pandas
- Read in Excel Data Using Pandas
5- Data Pre-Processing/Wrangling
- Removing NAs/No Values From Our Data
- Basic Data Handling: Starting with Conditional Data Selection
- Drop Column/Row
- Subset and Index Data
- Basic Data Grouping Based on Qualitative Attributes
- Crosstabulation
- Reshaping
- Pivoting
- Rank and Sort Data
- Concatenate
- Merging and Joining Data Frames
6- Introduction to Data Visualizations
- What is Data Visualization?
- Some Theoretical Principles Behind Data Visualization
- Histograms-Visualize the Distribution of Continuous Numerical Variables
- Boxplots-Visualize the Distribution of Continuous Numerical Variables
- Scatter Plot-Visualize the Relationship Between 2 Continuous Variables
- Barplot
- Pie Chart
- Line Chart
7- Statistical Data Analysis-Basic
- What is Statistical Data Analysis?
- Some Pointers on Collecting Data for Statistical Studies
- Some Pointers on Exploring Quantitative Data
- Explore the Quantitative Data: Descriptive Statistics
- Grouping & Summarizing Data by Categories
- Visualize Descriptive Statistics-Boxplots
- Common Terms Relating to Descriptive Statistics
- Data Distribution- Normal Distribution
- Check for Normal Distribution
- Standard Normal Distribution and Z-scores
- Confidence Interval-Theory
- Confidence Interval-Calculation
8- Statistical Inference
- What is Hypothesis Testing?
- Test the Difference Between Two Groups
- Test the Difference Between More Than Two Groups
Programme scope, delivery arrangements and suitability are confirmed during enquiry.