# Python for Data Science

Understanding the importance of Python as a data science tool is crucial for anyone aspiring to leverage data effectively. This course is designed to equip you with the essential skills and knowledge needed to thrive in the field of data science.

This course teaches the vital skills to manipulate data using pandas, perform statistical analyses, and create impactful visualizations. Learn to solve real-world business problems and prepare data for machine learning applications. Get ready for some challenging assessments in the Python course where you’ll apply your skills to real-world scenarios, ensuring a rewarding learning experience. Join us and Enroll in this course and take a step into the world of data-driven discoveries. No previous experience required.

### Introduction to Python for Data Science

##### 12 videos Total 60 minutes

- Welcome to python for data science 5 minutes Preview module
- Expert Talk – A data scientist’s experience with Python 3 minutes
- What is python?3 minutes
- Working with Jupyter notebooks7 minutes
- Introduction to the problem4 minutes
- Solution approach – Preparing tables and charts3 minutes
- Solution approach – Gaining Insights4 minutes
- Solution Approach – Airline traffic analysis4 minutes
- Solution summary3 minutes
- Expert Talk – Why Python is the language of choice for data science professionals9 minutes
- Introduction to the Problem4 minutes
- Exploring the Problem4 minutes

##### 6 readings Total 60 minutes

- Course syllabus10 minutes
- Installation guide 10 minutes
- Working effectively with Jupyter notebooks10 minutes
- Important note!10 minutes
- The Global Problem Statement10 minutes
- Tell us what you think!10 minutes

##### 2 quizzes Total 60 minutes

- Python fundamentals30 minutes
- Data Analysis 30 minutes

### Data Wrangling with Python

##### 32 videos Total 174 minutes

- Introduction0 minutesPreview module
- Diving into CSV Data6 minutes
- Data inspection5 minutes
- Finding missing data in the POS data6 minutes
- Deleting missing data and saving the cleaned data set7 minutes
- Lab data and problem3 minutes
- A note on assessments0 minutes
- Basic data structures – lists and dictionaries13 minutes
- Basic data structures – series3 minutes
- Creating a data frame using lists, dictionaries and series3 minutes
- Slicing with precision5 minutes
- Changing the indices and saving the new DataFrame4 minutes
- Navigating data insights5 minutes
- Selecting data that match certain criteria4 minutes
- Selecting data that match multiple criteria3 minutes
- Expert Talk – Understanding your data5 minutes
- What are the unique products in the POS data set?6 minutes
- Finding specific values in the data7 minutes
- How much did we sell per category? 5 minutes
- Finding totals and averages by brand and by category6 minutes
- Grouping by multiple attributes5 minutes
- Displaying aggregated data in a pivot table8 minutes
- Expert talk – How insights and data analysis guide each other5 minutes
- Working with dates6 minutes
- How much did we sell each month?6 minutes
- What is the monthly average of sales?5 minutes
- Were there specific dates when sales were high?8 minutes
- What if we have more than one dataset?5 minutes
- Merging some simple data sets5 minutes
- Merging POS data with the online data6 minutes
- Walkthrough – How to approach a graded assignment3 minutes
- Summary1 minute

##### 4 readings Total 60 minutes

- Data cleaning with python10 minutes
- Resources – Datasets and Jupyter notebooks 10 minutes
- Python statistics fundamentals 10 minutes
- Working with dates 30 minutes

##### 6 quizzes Total 180 minutes

- DataFame essentials30 minutes
- DataFrame operations30 minutes
- Data selection & filtering30 minutes
- Data manipulation & aggregation30 minutes
- Date time operations30 minutes
- Merging & joining dataframes30 minutes

##### 2 programming assignments Total 300 minutes

- Graded Assignment120 minutes
- New Programming Assignment180 minutes

##### 5 ungraded labs Total 150 minutes

- Data cleaning & manipulation 30 minutes
- Data slicing & manipulations30 minutes
- Data aggregations30 minutes
- Practice Programming Assignment30 minutes
- Merging the data30 minutes

### Exploratory Data Analysis

##### 34 videos Total 205 minutes

- Introduction0 minutesPreview module
- Expert Talk – Why EDA is a superpower6 minutes
- Finding the average of the data6 minutes
- Understanding the spread of the data9 minutes
- Quantiles – how to understand and visualize them7 minutes
- Exploring variability in the POS data6 minutes
- What shape is my data? 6 minutes
- Understanding the distributions of features in the POS data6 minutes
- Understanding Data Distributions4 minutes
- Some other common shapes of data – Part I10 minutes
- Some other common shapes of data – Part I6 minutes
- Some other common shapes of data – Part II8 minutes
- Some other common shapes of data – Part III8 minutes
- What chance of revenue falls in a given range3 minutes
- How are the features related to each other? – Part I5 minutes
- How are the features related to each other? – Part I5 minutes
- How are the features related to each other? – Part II4 minutes
- How are the features related to each other? – Part II5 minutes
- Visualizing categorical features6 minutes
- Visualizing proportions7 minutes
- Expert Talk – Power of visualization & its importance in storytelling 7 minutes
- Using boxplots to compare revenues across segments in the POS data7 minutes
- Making better visuals – Part III9 minutes
- Communicating insights better by creating multiple subplots within the same plot2 minutes
- Comparing the distribution of revenue for each sector by overlaying their KDE plots 7 minutes
- Sampling our data – Part I 5 minutes
- Sampling our data – Part II4 minutes
- Introduction to hypothesis testing – Part I5 minutes
- Introduction to hypothesis testing – Part II4 minutes
- Hypothesis testing using Z – Test – Part I6 minutes
- Hypothesis testing using Z – Test – Part II5 minutes
- Hypothesis testing using t – Test6 minutes
- Hypothesis testing using Chi-square test7 minutes
- Summary1 minute

##### 1 reading Total 10 minutes

- Resources – Datasets and Jupyter notebooks10 minutes

##### 5 quizzes Total 150 minutes

- Statistics fundamentals30 minutes
- Data distributions30 minutes
- Understanding relationships between features30 minutes
- Practice Quiz30 minutes
- Practice quiz30 minutes

##### 1 programming assignment Total 120 minutes

- Graded Assignment120 minutes

##### 4 ungraded labs Total 120 minutes

- Understanding data distributions 30 minutes
- Practice Programming Assignment30 minutes
- Practice Programming Assignment30 minutes
- Practice Programming Assignment30 minutes

### Data Pre Processing

##### 25 videos Total 134 minutes

- Introduction4 minutesPreview module
- Expert Talk – Handling missing data7 minutes
- What to do with missing values?5 minutes
- Missing values in the POS data2 minutes
- Missing values within a hierarchy7 minutes
- Missing values within a hierarchy (contd.)5 minutes
- What if parts of the hierarchy are also missing?2 minutes
- Finishing up missing value treatment in the POS data5 minutes
- Missing values – another simpler example8 minutes
- Working with categoric features4 minutes
- Transforming features – binning and discretization8 minutes
- Transforming features – binning and discretization (contd.)6 minutes
- Encoding categoric features – one-hot and label encoding9 minutes
- Encoding features in the POS data5 minutes
- Finishing up the encoding and saving the encoded data3 minutes
- What is data normalization and why do we need it?4 minutes
- Data normalization using min-max scaling5 minutes
- Data normalization using z-score scaling4 minutes
- Other types of data transformation4 minutes
- Applying log transformation to the online data4 minutes
- Finding outlying data5 minutes
- Removing outliers by dropping them4 minutes
- How to deal with outliers – imputation6 minutes
- How to deal with outliers – capping4 minutes
- Summary2 minutes

##### 2 readings Total 40 minutes

- Resources – Datasets and Jupyter notebooks10 minutes
- Data pre-processing 30 minutes

##### 3 quizzes Total 90 minutes

- Missing values30 minutes
- Dealing with categorical data30 minutes
- Data normalization30 minutes

##### 1 programming assignment Total 120 minutes

- Graded Assignment120 minutes

##### 3 ungraded labs Total 90 minutes

- Handling missing values30 minutes
- Handling categorical features30 minutes
- Data normalization & treating outliers30 minutes

### Feature Engineering

##### 11 videos Total 53 minutes

- Introduction1 minutePreview module
- Reducing the dimensionality of data sets6 minutes
- Exploring the features of the obesity data set7 minutes
- What is Principal Component Analysis(PCA)?7 minutes
- Applying PCA to the obesity data4 minutes
- Creating a transformed version of the data through feature engineering8 minutes
- Expert Talk – Gen AI in Python4 minutes
- Introduction to Gen AI in Python for Data science3 minutes
- Some quick data analysis using PandasAI4 minutes
- Some quick data visualization using PandasAI3 minutes
- Summary1 minute

##### 2 readings Total 40 minutes

- Complete guide to Feature Engineering30 minutes
- Resources – Datasets and Jupyter notebooks10 minutes

##### 1 quiz Total 30 minutes

- Feature engineering & PCA30 minutes

##### 1 programming assignment Total 120 minutes

- Graded Assignment120 minutes

##### 1 ungraded lab Total 30 minutes

- Dimensionality reduction, PCA30 minutes