Python is a high level dynamic programming language. Using python, programming becomes more simpler as it requires just few lines to code. By taking this course one will have some basic knowledge about Python Programming.
- Contents
- What is Python & History
- Installing Python & Python Environment
- Basic commands in Python
- Data Types and Operations
- Python packages
- My first python program
- If-then-else statement
- Data importing and exporting
- Working with datasets
- Creating new variables
- Taking a random sample from data
- Descriptive statistics
- Quartiles, Percentiles
- Box Plots and Graphs
- After completion of this course, one will have good knowledge on python installation, data handing, basic Python commands, etc.
Course Curriculum
| Session 1 - Introduction to Python | |||
| Handout – Introduction to Python | 00:00:00 | ||
| 1.1 Python Intoduction and IDE | FREE | 00:00:00 | |
| 1.2 Basic Commands in Python | FREE | 00:00:00 | |
| 1.3 Objects, Number and Strings | 00:00:00 | ||
| 1.4 Objects, List, Tuples and Dictionaries | 00:00:00 | ||
| 1.5 If_else and For_loop | 00:00:00 | ||
| 1.6 Functions and Packages | 00:00:00 | ||
| 1.7 Important Packages | 00:00:00 | ||
| 1.8 End Note | 00:00:00 | ||
| Python Quiz Unit 1 | Unlimited | ||
| Session 2 - Data Handling in Python | |||
| Handout – Data Handling in Python | 00:00:00 | ||
| 2.1 Introduction to DataHandling | FREE | 00:00:00 | |
| 2.2 Basic Commands and Checklist | FREE | 00:00:00 | |
| 2.3 Subsetting the Dataset | 00:00:00 | ||
| 2.4 Calculated Field Sort Duplicates | 00:00:00 | ||
| 2.5 Merge and Exporting | 00:00:00 | ||
| Python Quiz Unit 2 | Unlimited | ||
| Session 3 - Basic Statistics, Graphs and Reports in Python | |||
| Handout – Basic Statistics, Graphs and Reports in Python | 00:00:00 | ||
| 3.1 Basic Statistics and Sampling | FREE | 00:00:00 | |
| 3.2 Discriptive Statistics | 00:00:00 | ||
| 3.3 Percentile and Boxplot | 00:00:00 | ||
| 3.4 Graphs Plots and Conclusion | 00:00:00 | ||
| Python Quiz Unit 3 | Unlimited | ||
| Session 4 - Data Cleaning and Treatement | |||
| Handout – Data Cleaning and Treatement | 00:00:00 | ||
| 4.1 Data Cleaning Intro and Model Building Cycle | FREE | 00:00:00 | |
| 4.2 Model Building Cycle | FREE | 00:00:00 | |
| 4.3 Data Cleaning Case Study | 00:00:00 | ||
| 4.4 Step1 Basic Content of Dataset | 00:00:00 | ||
| 4.5 Variable Level Exploration Catagorical | 00:00:00 | ||
| 4.6 Reading Data Dictionary | 00:00:00 | ||
| 4.7 LAB – Step2 Catagorical Variable Exploration | 00:00:00 | ||
| 4.8 Step3 Variable Level Exploration – Continuous | 00:00:00 | ||
| 4.8.1 LAB – Step3 Variable Level Exploration | 00:00:00 | ||
| 4.9 Data Cleaning and Treatments | 00:00:00 | ||
| 4.10 Step4 Treatment – Scenario1 | 00:00:00 | ||
| 4.10.1 LAB – step4 Treatment – scenario 1 | 00:00:00 | ||
| 4.11 Step4 Treatment – Scenario2 | 00:00:00 | ||
| 4.11.1 LAB – step4 Treatment – scenario 2 | 00:00:00 | ||
| 4.12 Data Cleaning – Scenario 3 | 00:00:00 | ||
| 4.12.1 LAB – Data Cleaning – Scenario 3 | 00:00:00 | ||
| 4.13 Some Other Variables | 00:00:00 | ||
| 4.14 Conclusion | 00:00:00 | ||
Course Reviews
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190 STUDENTS ENROLLED
Overall
Overall good to build an interest in data analytics areas. I learned something new out of it.because it is mixture of theories and practical as well. It covered some libraries like numpy, pandas, matplotlib, and various plots like Bar, scatter graph, boxed graph.
In short : simple, in own language with subtitle, easy to understand…!