### About This Course

**Latest update – November 2020**

In this course, you’ll learn how to get started in Data Science. You don’t need any prior knowledge in programming. We’ll teach you the Python basics you need to get started.

Here are the items we’ll cover in this course:

- The Data Science Process
- Python for Data Science
- NumPy for Numerical Computation
- Pandas for Data Manipulation
- Matplotlib for Visualization
- Seaborn for Beautiful Visuals
- Plotly for Interactive Visuals
- Introduction to Machine Learning
- Dask for Big Data
- Association Rule Mining – Apriori
- Deep Learning and next steps

For the machine learning section, here are some items we’ll cover:

- How algorithms work
- Advantages and disadvantages of various algorithms
- Feature Importances
- Metrics
- Cross-Validation
- Fighting Overfitting
- Hyperparameter Tuning
- Handling Imbalanced Data
- TensorFlow And Keras
- Automated Machine Learning (AutoML)

Who this course is for:

- People who would like to get started with Data Science and Machine Learning without any prior knowledge
- People who would like to refresh their Data Science and Machine Learning knowledge

**Our Promise to You**

By the end of this course, you will have learned Python for Data Science and Machine Learning.

**30 Day Money Back Guarantee.** If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

**Get started today and learn more about Data Science and Machine Learning.**

### Course Curriculum

Section 1 - Introduction | |||

Plan Of Attack | 00:00:00 | ||

Downloadable Resources | 00:00:00 | ||

Install Anaconda | 00:00:00 | ||

Understand The Data Science Process | 00:00:00 | ||

Section 2 - Understand Python For Data Science | |||

Python For Data Science | 00:00:00 | ||

Linux Launch Notebook | 00:00:00 | ||

Windows Launch Notebook | 00:00:00 | ||

Folder Structure | 00:00:00 | ||

Python Operations And Comments | 00:00:00 | ||

Python Data Types | 00:00:00 | ||

Python Lists | 00:00:00 | ||

Lists – Negative Indexing | 00:00:00 | ||

Python Dictionaries | 00:00:00 | ||

Python Tuples | 00:00:00 | ||

Python Sets | 00:00:00 | ||

Python Boolean Type | 00:00:00 | ||

Conditional Statements | 00:00:00 | ||

Python Functions | 00:00:00 | ||

Python For Loop | 00:00:00 | ||

Python While Loop | 00:00:00 | ||

Python Map Function | 00:00:00 | ||

Python Range Function | 00:00:00 | ||

Python Exercise | 00:00:00 | ||

Python Project Solutions | 00:00:00 | ||

Section 3 - Package Management | |||

Package Management Introduction | 00:00:00 | ||

pip And Virtualenv Intuition | 00:00:00 | ||

pip And Virtualenv Practical | 00:00:00 | ||

Installing Packages Using The Anaconda Navigator | 00:00:00 | ||

Section 4 - NumPy For Numerical Computation | |||

NumPy For Numerical Computation Introduction | 00:00:00 | ||

NumPy Introduction | 00:00:00 | ||

NumPy Arrays | 00:00:00 | ||

Checking Documentation In Notebooks | 00:00:00 | ||

Indexing One Dimensional Array | 00:00:00 | ||

Indexing Multi-Dimensional Array | 00:00:00 | ||

Broadcasting In NumPy | 00:00:00 | ||

NumPy Operations | 00:00:00 | ||

NumPy Project | 00:00:00 | ||

NumPy Project Solutions | 00:00:00 | ||

Section 5 - Manipulate Data Using Pandas | |||

Manipulate Data Using Pandas Introduction | 00:00:00 | ||

Pandas Introduction | 00:00:00 | ||

Pandas Dataframe | 00:00:00 | ||

Resetting The Index | 00:00:00 | ||

Deleting Columns | 00:00:00 | ||

Dealing With Null Values | 00:00:00 | ||

Creating New Columns | 00:00:00 | ||

Selecting In Pandas | 00:00:00 | ||

Grouping Data | 00:00:00 | ||

Exporting A Pandas Dataframe | 00:00:00 | ||

Loading Datasets | 00:00:00 | ||

Creating Pivot Tables | 00:00:00 | ||

Pandas Project | 00:00:00 | ||

Section 6 - Pandas Project Solutions | |||

Part 1 | 00:00:00 | ||

Part 2 | 00:00:00 | ||

Part 3 | 00:00:00 | ||

Part 4 | 00:00:00 | ||

Part 5 | 00:00:00 | ||

Part 6 | 00:00:00 | ||

Part 7 | 00:00:00 | ||

Section 7 - Data Visualization In Matplotlib | |||

Data Visualization In Matplotlib Introduction | 00:00:00 | ||

Matplotlib Vertical Bar Plot | 00:00:00 | ||

Matplotlib Horizontal Bar Plot | 00:00:00 | ||

Matplotlib Scatter Plot | 00:00:00 | ||

Matplotlib Histogram | 00:00:00 | ||

Matplotlib Pie Chart | 00:00:00 | ||

Matplotlib Line Plot | 00:00:00 | ||

Matplotlib Subplots | 00:00:00 | ||

Matplotlib Figure And Axes Part one | 00:00:00 | ||

Matplotlib Figure And Axes Part Two | 00:00:00 | ||

Matplotlib Project And Solutions | 00:00:00 | ||

Section 8 - Data Visualization In Seaborn - Categorical Plots | |||

Seaborn Count Plot | 00:00:00 | ||

Seaborn Violin Plot | 00:00:00 | ||

Seaborn – Adding Hue | 00:00:00 | ||

Seaborn Strip Plot | 00:00:00 | ||

Swarm Plot With Hue | 00:00:00 | ||

Seaborn Order X Values | 00:00:00 | ||

Strip Plot with Hue | 00:00:00 | ||

Seaborn Boxplot | 00:00:00 | ||

Seaborn Boxen Plot | 00:00:00 | ||

Seaborn Barplot | 00:00:00 | ||

Section 9 - Data Visualization In Seaborn - Visualizing Distributions | |||

Joint And Scatter Plots | 00:00:00 | ||

Seaborn Hexagonal Bins & Kernel Density Estimation | 00:00:00 | ||

Seaborn Distplot | 00:00:00 | ||

Seaborn Pair Plot | 00:00:00 | ||

Seaborn Line Plot | 00:00:00 | ||

Section 10 - Seaborn With Matplotlib Subplots | |||

Subplots In Seaborn | 00:00:00 | ||

Seaborn Subplots With Figure And Axes | 00:00:00 | ||

Section 11 - Matrix Visualization In Seaborn | |||

Seaborn Heatmap | 00:00:00 | ||

Section 12 - Visualize Linear Relationships In Seaborn | |||

Regression Plots In Seaborn | 00:00:00 | ||

Seaborn Jointplot With Regression | 00:00:00 | ||

Section 13 - Seaborn Multi-Plot Grids | |||

Seaborn FacetGrid | 00:00:00 | ||

Seaborn PairGrid | 00:00:00 | ||

Section 14 - Word Cloud | |||

Visualization Using Word Clouds | 00:00:00 | ||

Seaborn And Word Cloud Exercise And Solutions | 00:00:00 | ||

Section 15 - Build Interactive Visuals With Plotly | |||

Plotly Introduction | 00:00:00 | ||

Plotly And Jupyter Notebooks | 00:00:00 | ||

Plotly Express | 00:00:00 | ||

Plotly Line Plot | 00:00:00 | ||

Plotly Bar Plot | 00:00:00 | ||

Plotly Animations | 00:00:00 | ||

Plotly Density Heatmap | 00:00:00 | ||

Visualizing On Maps Using Plotly | 00:00:00 | ||

Subplots In Plotly | 00:00:00 | ||

Plotly Project And Solutions | 00:00:00 | ||

Section 16 - Interactive Web Applications With Dash | |||

Interactive Web Applications With Dash Introduction | 00:00:00 | ||

Dash Introduction | 00:00:00 | ||

Install Packages | 00:00:00 | ||

First Application | 00:00:00 | ||

Styles | 00:00:00 | ||

Dash Callbacks | 00:00:00 | ||

Drop Down Component | 00:00:00 | ||

Custom CSS | 00:00:00 | ||

Change Title | 00:00:00 | ||

Data Upload | 00:00:00 | ||

Drag And Drop – Table | 00:00:00 | ||

Drag And Drop – Visualize | 00:00:00 | ||

Prevent Update | 00:00:00 | ||

Sunburst | 00:00:00 | ||

Div Data Store – Share Data Between Two Callbacks | 00:00:00 | ||

Get Hover, Select And Click Data | 00:00:00 | ||

Change Pie Chart From Hover Data | 00:00:00 | ||

Dash Tabs | 00:00:00 | ||

Single Date Picker | 00:00:00 | ||

Dash Date Range Picker | 00:00:00 | ||

Caching In Dash | 00:00:00 | ||

Loading Animations | 00:00:00 | ||

Live Updates | 00:00:00 | ||

Plotly Animations | 00:00:00 | ||

Mapping | 00:00:00 | ||

Multiple Pages Version 1 | 00:00:00 | ||

Multiple Pages Version 2 | 00:00:00 | ||

Basic Authentication | 00:00:00 | ||

Host On Heroku | 00:00:00 | ||

Dash Dev Tools | 00:00:00 | ||

Chart Studio | 00:00:00 | ||

Dash Libraries | 00:00:00 | ||

Section 17 - Building Dashboard With Power BI Desktop | |||

Introduction To Power BI | 00:00:00 | ||

Virtual Box | 00:00:00 | ||

File Sharing | 00:00:00 | ||

Power BI Dashboard And Overview | 00:00:00 | ||

Stacked Column Chart | 00:00:00 | ||

Clustered Bar Chart | 00:00:00 | ||

100% Stacked Bar Chart | 00:00:00 | ||

100% Stacked Column Chart | 00:00:00 | ||

Line Chart | 00:00:00 | ||

Area Chart | 00:00:00 | ||

Stacked Area Chart | 00:00:00 | ||

Line And Stacked Column Chart | 00:00:00 | ||

Ribbon Chart | 00:00:00 | ||

Water Fall Chart | 00:00:00 | ||

Funnel Chart | 00:00:00 | ||

Scatterplot | 00:00:00 | ||

Pie Chart | 00:00:00 | ||

Donut Chart | 00:00:00 | ||

Treemap | 00:00:00 | ||

Map | 00:00:00 | ||

Gauge And Slicer | 00:00:00 | ||

Card | 00:00:00 | ||

Multi-Row Card | 00:00:00 | ||

Matrix | 00:00:00 | ||

Table | 00:00:00 | ||

Dashboard | 00:00:00 | ||

Project | 00:00:00 | ||

Section 18 - Data Visualization With Google Data Studio | |||

Data Visualization With Google Data Studio Introduction | 00:00:00 | ||

Data Studio Introduction | 00:00:00 | ||

Creating A Data Source | 00:00:00 | ||

Table Styling | 00:00:00 | ||

Data Freshness | 00:00:00 | ||

Add A Logo | 00:00:00 | ||

Add Scorecards | 00:00:00 | ||

Adding Filters | 00:00:00 | ||

Download Data | 00:00:00 | ||

Create New Pages | 00:00:00 | ||

Create Combo Chart | 00:00:00 | ||

Create A Pie Chart | 00:00:00 | ||

Add A Google Map | 00:00:00 | ||

Create A Scatter Plot | 00:00:00 | ||

Create Area Chart | 00:00:00 | ||

Create A Pivot Table | 00:00:00 | ||

Create A Treemap | 00:00:00 | ||

Table With Bars | 00:00:00 | ||

Table With Heatmap | 00:00:00 | ||

Stacked Column Chart | 00:00:00 | ||

Duplicate Page | 00:00:00 | ||

100% Stacked Bar Chart | 00:00:00 | ||

Donut Chart | 00:00:00 | ||

Use Community Visualizations | 00:00:00 | ||

Create A Dashboard | 00:00:00 | ||

The Google Data Studio Explorer | 00:00:00 | ||

Embed Report To Website | 00:00:00 | ||

Section 19 - Supervised Machine Learning | |||

Supervised Machine Learning Introduction | 00:00:00 | ||

Introduction To Machine Learning | 00:00:00 | ||

Linear Regression Intuition | 00:00:00 | ||

Linear Regression In Scikit-Learn | 00:00:00 | ||

Linear Regression Exercise | 00:00:00 | ||

Linear Regression Solutions | 00:00:00 | ||

Logistic Regression Intuition | 00:00:00 | ||

Logistic Regression In Python | 00:00:00 | ||

Logistic Regression Project | 00:00:00 | ||

Logistic Regression Solutions | 00:00:00 | ||

Decision Trees Intuition | 00:00:00 | ||

Random Forest Intuition | 00:00:00 | ||

Decision Tree And Random Forest Classifier In Scikit-Learn | 00:00:00 | ||

Decision Tree And Random Forest Classification Project | 00:00:00 | ||

Decision Tree And Random Forest Classifier Solutions | 00:00:00 | ||

Decision Tree And Random Forest Regression Part 1 | 00:00:00 | ||

Decision Tree And Random Forest Regression Part 2 | 00:00:00 | ||

Random Forest Regression Part 3 – Feature Importance | 00:00:00 | ||

Visualize Tree In Random Forest Regression | 00:00:00 | ||

Random Forest Regression Exercise | 00:00:00 | ||

Random Forest Regression Solutions | 00:00:00 | ||

KNeighbors Intuition | 00:00:00 | ||

K Nearest Neighbors – Getting Started | 00:00:00 | ||

Checking For Outliers | 00:00:00 | ||

More Exploratory Data Analysis | 00:00:00 | ||

Student And Income Plots | 00:00:00 | ||

Peasonr – Relationship Between The Income And Balance | 00:00:00 | ||

Chi Square Test – Relationship Between Defaulting And Being A Student | 00:00:00 | ||

T-Test – Is The mean Income Of Both Defaulters And Non Defaulters The Same? | 00:00:00 | ||

Feature Engineering | 00:00:00 | ||

KNN Implementation In Python | 00:00:00 | ||

Support Vector Machines Intuition | 00:00:00 | ||

Support Vector Classifier In Python | 00:00:00 | ||

Support Vector Machine Exercise And Solutions | 00:00:00 | ||

Handling Imbalanced Data | 00:00:00 | ||

LightGBM Intuition | 00:00:00 | ||

Dask For Loading Large Datasets | 00:00:00 | ||

Dask Intuition | 00:00:00 | ||

LightGBM Classifier | 00:00:00 | ||

LightGBM Classifier Project | 00:00:00 | ||

LightGBM Classifier Project Solutions | 00:00:00 | ||

LightGBM Regressor | 00:00:00 | ||

LightGBM Regressor Project | 00:00:00 | ||

LightGBM Regressor Project Solutions | 00:00:00 | ||

Extreme Gradient Boosting | 00:00:00 | ||

XGBoost Classifier | 00:00:00 | ||

XGBoost Classifier Project | 00:00:00 | ||

XGBoost Classifier Project Solutions | 00:00:00 | ||

XGBoost Regressor | 00:00:00 | ||

XGBoost Regressor Project | 00:00:00 | ||

XGBoost Regressor Solutions | 00:00:00 | ||

Tuning And Model Selection | 00:00:00 | ||

CatBoost Intuition | 00:00:00 | ||

CatBoost Part Two | 00:00:00 | ||

CatBoost Classifier | 00:00:00 | ||

CatBoost Classifier Exercise | 00:00:00 | ||

CatBoost Classifier Project Solutions | 00:00:00 | ||

Grid Search CV And Model Selection | 00:00:00 | ||

CatBoost Regression | 00:00:00 | ||

CatBoost Regression Exercise | 00:00:00 | ||

CatBoost Regression Project Solutions | 00:00:00 | ||

Time Series Analysis | 00:00:00 | ||

Time Series Exercise | 00:00:00 | ||

Time Series Project Solutions | 00:00:00 | ||

Section 20 - K-Means - Unsupervised Machine Learning | |||

Unsupervised Machine Learning Introduction | 00:00:00 | ||

K-Means CLustering Intuition | 00:00:00 | ||

Loading Packages | 00:00:00 | ||

Convert The Data To Dummy Variables | 00:00:00 | ||

Principal Component Analysis | 00:00:00 | ||

Data Scaling | 00:00:00 | ||

K-Means Implementation | 00:00:00 | ||

Selecting the Best Number of Clusters | 00:00:00 | ||

Cluster Analysis | 00:00:00 | ||

K-means Exercise | 00:00:00 | ||

K-Means Exercise Solutions | 00:00:00 | ||

Section 21 - Feature Ranking With Recursive Feature Elimination | |||

Introduction | 00:00:00 | ||

Feature Ranking And Selection | 00:00:00 | ||

Recursive Feature Implementation | 00:00:00 | ||

Creating A Pipeline | 00:00:00 | ||

Repeated Stratified K Fold | 00:00:00 | ||

Fitting The Pipeline | 00:00:00 | ||

Automatic Feature Selection | 00:00:00 | ||

Exercise | 00:00:00 | ||

Section 22 - Association Rule Mining With Apriori | |||

Association Rule Mining With Apriori Introduction | 00:00:00 | ||

Introduction | 00:00:00 | ||

Apriori Data Preparation | 00:00:00 | ||

Apriori Implementation | 00:00:00 | ||

Apriori Solutions | 00:00:00 | ||

Section 23 - Building Data Science Applications | |||

Introduction | 00:00:00 | ||

StreamLit | 00:00:00 | ||

StreamLit Implementation | 00:00:00 | ||

Heroku Sign Up | 00:00:00 | ||

Heroku Hosting | 00:00:00 | ||

Heroku Data Application Project | 00:00:00 | ||

Section 24 - Natural Language Processing | |||

Installing Natural Language Toolkit | 00:00:00 | ||

Natural Language Processing | 00:00:00 | ||

Importing Packages | 00:00:00 | ||

Loading Data | 00:00:00 | ||

Cleaning Data | 00:00:00 | ||

Removing Stop Words | 00:00:00 | ||

Lemmatizing | 00:00:00 | ||

A Bag Of Words Model | 00:00:00 | ||

Occurrences To Frequencies | 00:00:00 | ||

Fit To Model | 00:00:00 | ||

Save The Model | 00:00:00 | ||

Flask | 00:00:00 | ||

Flask Logic | 00:00:00 | ||

HTML | 00:00:00 | ||

Procfile | 00:00:00 | ||

Heroku Deployment | 00:00:00 | ||

Project | 00:00:00 | ||

Section 25 - Automated Machine Learning | |||

Introduction | 00:00:00 | ||

Auto-ML Intuition | 00:00:00 | ||

Auto-ML Google Collaboration | 00:00:00 | ||

Exercise | 00:00:00 | ||

Section 26 - Big Data Analysis With Apache Spark | |||

Apache Spark Introduction | 00:00:00 | ||

Apache Spark Intuition | 00:00:00 | ||

Google Colab Installation | 00:00:00 | ||

Using Spark – Resilient Distributed Datasets (RDD) | 00:00:00 | ||

Refresher Of Important Concepts – Map, Filter, Reduce, Lambda | 00:00:00 | ||

Applying The Concepts In Spark | 00:00:00 | ||

Apache Spark DataFrames | 00:00:00 | ||

Renaming A Column In Spark DataFrame | 00:00:00 | ||

Selecting Columns In A DataFrame | 00:00:00 | ||

Filtering A DataFrame | 00:00:00 | ||

Group By DataFrame Operation | 00:00:00 | ||

SQL Queries In Spark | 00:00:00 | ||

Spark On Databricks | 00:00:00 | ||

Machine Learning In PySpark – Code Along Project | 00:00:00 | ||

Local Set Up – Set Up Ubuntu Virtual Machine | 00:00:00 | ||

Local Set Up – Install Docker And The Spark Jupyter Container | 00:00:00 | ||

Section 27 - Deep Learning And Next Steps | |||

Deep Learning And Next Steps Introduction | 00:00:00 | ||

Core Concepts | 00:00:00 | ||

Scikit-Learn | 00:00:00 | ||

Scikit-Learn Neural Networks | 00:00:00 | ||

Exercise | 00:00:00 | ||

Solutions | 00:00:00 | ||

Regression In Scikit-Learn Neural Networks | 00:00:00 | ||

Exercise | 00:00:00 | ||

Solutions | 00:00:00 | ||

Points To Note | 00:00:00 | ||

Data Preprocessing | 00:00:00 | ||

Building The Network | 00:00:00 | ||

Evaluating The Model | 00:00:00 | ||

Plotting The Model Loss | 00:00:00 | ||

Overfitting – Classification | 00:00:00 | ||

Keras Callbacks | 00:00:00 | ||

Custom Keras Callbacks | 00:00:00 | ||

Visualization In TensorFlow – TensorBoard | 00:00:00 | ||

Saving The Model | 00:00:00 | ||

Convolutional Neural Network Concepts | 00:00:00 | ||

Building A Convolutional Neural Network | 00:00:00 | ||

Section 28 - Congratulations | |||

Final Thoughts | 00:00:00 |