Learn Data Science Skills: Python, Pandas, Machine Learning

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1. Introduction to Data Science

1. Introduction.html

2. What is Data Science.html

3. Importance and applications of data science in various industries..html

4. Overview of tools and technologies used in data science..html

5. Introduction to Python programming language..html

6. Essential libraries for data manipulation and analysis (e.g., Pandas, NumPy)..html

7. Ethics and Best Practices in Data Science.html

2. Environment Setup

1. Python Installation on Windows.mp4

03:47

2. What are virtual environments.mp4

01:51

3. Creating and activating a virtual environment on Windows.mp4

06:43

4. Python Installation on macOS.html

5. Creating and activating a virtual environment on macOS.html

6. What is Jupyter Notebook.html

7. Installing Pandas and Jupyter Notebook in the Virtual Environment.mp4

01:06

8. Starting Jupyter Notebook.mp4

05:23

9. Exploring Jupyter Notebook Server Dashboard Interface.mp4

04:00

10. Creating a new Notebook.mp4

02:55

11. Exploring Jupyter Notebook Source and Folder Files.mp4

04:37

12. Exploring the Notebook Interface.mp4

08:41

3. Data Manipulation and visualization with pandas

1. Overview of Pandas.html

2. Pandas Data Structures.html

3. Creating a Pandas Series from a List.mp4

06:04

4. Creating a Pandas Series from a List with Custom Index.mp4

02:28

5. Creating a pandas series from a Python Dictionary.mp4

03:34

6. Accessing Data in a Series using the index by label.mp4

02:14

7. Accessing Data in a Series By position.mp4

02:15

8. Slicing a Series by Label.mp4

02:41

9. Creating a DataFrame from a dictionary of lists.mp4

06:31

10. Creating a DataFrame From a list of dictionaries.mp4

05:03

11. Accessing data in a DataFrame.mp4

07:45

12. Download Dataset.mp4

01:23

13. Loading Dataset into a DataFrame.mp4

03:40

14. Inspecting the data.mp4

03:09

15. Data Cleaning.mp4

06:30

16. Data transformation and analysis.mp4

07:24

17. Visualizing data.mp4

09:02

4. Machine Learning Build ,Train and deploy a machine learning model

1. What is Machine Learning.html

2.1 Importing+Libaries+and+modules.txt

2. Installing and importing libraries.mp4

05:57

3. Data Preprocessing.html

4. What is a Dataset.html

5. Downloading dataset.mp4

04:15

6. Exploring the Dataset.mp4

05:26

7.1 handling+missing+values.txt

7. Handle missing values and drop unnecessary columns..mp4

06:26

8.1 Encode+categorical+variables..txt

8. Encode categorical variables..mp4

07:37

9. What is Feature Engineering.html

10.1 Create+new+features..txt

10. Create new features..mp4

08:15

11.1 Dropping+unnecessary+columns.txt

11. Dropping unnecessary columns.mp4

03:59

12.1 bar+plot+code.txt

12. Visualize survival rate by gender.mp4

06:55

13.1 bar+plot+2.txt

13. Visualize survival rate by class.mp4

04:05

14.1 visualize+numeric+data.txt

14. Visualize numerical features.mp4

04:11

15.1 Visualize++the+distribution+of+Age.txt

15. Visualize the distribution of Age.mp4

04:47

16.1 Visualize+number+of+passengers+in+each+passenger+class.txt

16. Visualize number of passengers in each passenger class.mp4

03:49

17.1 countplot.txt

17. Visualize number of passengers that survived.mp4

03:48

18.1 heatmap.txt

18. Visualize the correlation matrix of numerical variables.mp4

06:04

19.1 Visualize++the+distribution+of++Fare..txt

19. Visualize the distribution of Fare..mp4

04:56

20. Data Preparation and Training Model.html

21. What is a Model.html

22.1 define+features.txt

22. Define features and target variable..mp4

04:56

23.1 Split+data.txt

23. Split data into training and testing set.mp4

02:55

24.1 Standardize+features.txt

24. Standardize features.mp4

03:55

25. What is a logistic regression model..html

26.1 regression+model.txt

26. Train logistic regression model..mp4

04:13

27. Making Predictions.mp4

03:28

28. What is accuracy in machine learning.html

29. What is confusion matrix..html

30. What is is classification report..html

31. What is a Heatmap.html

32.1 evaluate.txt

32. Evaluate the model using accuracy, confusion matrix, and classification report..mp4

06:02

33.1 confusion+matrix.txt

33. Visualize the confusion matrix..mp4

03:29

34.1 save+model.txt

34. Saving the Model.mp4

09:18

35.1 load+model.txt

35. Loading the model.mp4

05:03

36. Improving Understanding of the models prediction.mp4

05:31

37.1 DECISION+TREES.txt

37. Building a decision tree.mp4

07:45

38.1 RANDOM+FOREST.txt

38. Building a random forest.mp4

09:51

5. Predicting real house prices using machine learning

1.1 import+more+modules.txt

1. Importing Libraries and modules.mp4

07:28

2.1 load+housing+dataset.txt

2. Loading dataset and creating a dataframe.mp4

04:08

3. Checking for missing values.mp4

05:53

4. Dropping column and splitting data.mp4

06:07

5.1 standardize+features+for+housing+dataset.txt

5. Standardize the features for housing dataframe.mp4

04:00

6.1 model+training.txt

6. Initialize and train the regression model.mp4

02:54

7.1 predictions.txt

7. Make predictions on the test set..mp4

05:04

8.1 evaluate+housing+model.txt

8. Evaluating the model for the housing dataset..mp4

05:43

9.1 predicting++sample+data.txt

9. Predicting a small sample of data.mp4

09:11

10.1 scatter+plot+for+housing+data.txt

10. Creating scatter plot.mp4

08:14

11.1 housing+data+barplot.txt

11. Creating a bar plot.mp4

05:53

12.1 save+housing+model.txt

12. Saving the housing model.mp4

06:35

13. Loading the housing model.mp4

05:29

6. Build a Web App House Price Prediction Tool

1. What is Flask.html

2. Installing Flask.mp4

01:37

3. Installing Visual Studio Code.mp4

07:28

4. Creating a minimal flask app.mp4

05:39

5. How to run a flask app.mp4

01:24

6. Http and Https Methods.html

7. Loading the saved model and scaler into Python file.mp4

02:12

8. Define the home route.mp4

01:57

9. Define the prediction route.mp4

02:55

10. Creating the template.mp4

05:30

11. Adding a form to the template.mp4

03:17

12. Displaying predictions and clearing form inputs.mp4

04:22

13. Testing the prediction tool.mp4

01:51

14. Exploring deployment and hosting options.html

15. Create a new account on pythonanywhere.mp4

02:05

16. Creating a new web app in PythonAnywhere.mp4

01:53

17. Uploading project files to Pythonanywhere.mp4

02:30

18. Creating and activating a virtual environment on PythonAnywhere.mp4

03:06

19. What is a WSGI File.html

20. Configuring WSGI File.mp4

04:21

21. Running your app in a cloud hosting environment.mp4

03:48

22.1 app.zip

22.2 index.html

22.3 model scaler.zip

22. Project files.html

Description

Explore tools like : Python,Pandas,Jupyter Notebook, Numpy, Matplotlib, scikit-learn,Seaborn, Machine Learning

What You'll Learn?

Understand the fundamental concepts of data science.
Recognize the applications and industry impact of data science.
Utilize essential data science libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
Install Python and set up a development environment on Windows and macOS.
Understand the concept of virtual environments and create/manage them.
Familiarize with Jupyter Notebook and use it for interactive data analysis.
Explore and manipulate data using Pandas DataFrames.
Create and manipulate Pandas Series for efficient data handling
Load datasets into Pandas and perform initial data inspection and cleaning.
Transform and analyze data using Pandas methods.
Visualize data using Matplotlib and Seaborn for insights and reporting.
Understand supervised, unsupervised, and reinforcement learning techniques.
Preprocess data for machine learning models, including handling missing values and encoding categorical variables.
Build, train, and evaluate machine learning models using scikit-learn.
Measure model performance using metrics like accuracy, confusion matrix, and classification report.
Deploy a machine learning model for real-time predictions and understand model interpretability techniques.

Who is this for?

Aspiring Data Scientists

Students and Graduates

Professionals Transitioning Careers

Data Analysts and Engineers

Entrepreneurs and Business Owners

Anyone Curious About Data Science

What You Need to Know?

Basic Computer Literacy

Basic knowledge of algebra, including variables, equations, and basic operations.

No prior programming experience required, but familiarity with the basics of programming concepts (e.g., variables, loops, conditional statements) is beneficial.

Access to a computer with internet connectivity.

Ability to install software, including Python and necessary libraries (installation instructions will be provided)

More details

Description
Unlock the Power of Data Science Skills
In today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.
Course Overview
Our course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.
Essential Tools and Technologies
To equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python and R. Whether you're manipulating data with Pandas, performing numerical operations with NumPy, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.
Practical Skills Development
A significant focus of the course is hands-on learning.Â You'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources.Â You'll hone your ability to transform raw data into actionable insights that drive business decisions.
Environment Setup and Best Practices
Navigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll learn to create and manage virtual environments, enhancing your ability to work efficiently and maintain project dependencies.
Data Manipulation and Visualization Mastery
Central to effective data science is the ability to manipulate and visualize data effectively. Our course provides in-depth training in Pandas, where you'll learn to handle complex datasets, perform data transformations, and conduct exploratory data analysis. Through immersive visualization exercises, you'll discover how to communicate insights visually, making complex data accessible and actionable.
Machine Learning Fundamentals
Understanding machine learning is essential for any aspiring data scientist. You'll explore supervised, unsupervised, and reinforcement learning techniques, applying them to real-world datasets. From preprocessing data to training and evaluating machine learning models, you'll develop the skills needed to predict outcomes and optimize performance in various scenarios.
Real-world Applications and Projects
Throughout the course, you'll apply your newfound knowledge to practical projects that simulate real-world challenges. Whether it's predicting house prices using regression models or building a web app for interactive data analysis, these projects provide a platform to showcase your skills and build a professional portfolio.
Career Readiness and Support
Beyond technical skills, we prepare you for success in the competitive field of data science. You'll learn to interpret model performance metrics like accuracy and precision, communicate findings effectively through tools like the confusion matrix and classification reports, and understand the ethical implications of data-driven decisions.
Who Should Enroll?
This course is designed for anyone eager to embark on a journey into data science or enhance their existing skills:
Aspiring Data Scientists: Individuals looking to break into the field and build a strong foundation in data analysis and machine learning.
Professionals Seeking Career Advancement: Data analysts, engineers, and professionals from diverse industries seeking to expand their skill set and transition into data-driven roles.
Entrepreneurs and Business Owners: Leaders interested in leveraging data science to drive strategic decisions and gain a competitive edge in their industry.
Curious Learners: Enthusiasts with a passion for data-driven insights and a desire to understand the transformative potential of data science in todayâ€™s world.
Conclusion
By the end of this course, you'll have gained the confidence and skills needed to tackle complex data challenges with proficiency and precision. Whether you're looking to pivot your career, enhance your business acumen, or simply satisfy your curiosity about data science, our comprehensive curriculum and hands-on approach will empower you to unlock the power of data and chart your path to success.
Enroll today and embark on your journey to mastering data scienceâ€”one insightful discovery at a time.
Who this course is for:
Aspiring Data Scientists
Students and Graduates
Professionals Transitioning Careers
Data Analysts and Engineers
Entrepreneurs and Business Owners
Anyone Curious About Data Science

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We are experienced company that provides quality video based training .Our courses are easy to follow and understand and will take you froman absolute beginner with no technical skills to being efficient and confident with various technical skill like SQL and databases.We have worked with companies of various sizes and provided consultancy services at various levels.Thank you for learning with us and we hope your experience will be pleasant.

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