Data Science Full Course in Python & ChatGPT: All in One

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Md Shahriar

8:11:15

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1. Setting Up Your Data Analysis Platform

1.1 Mac.pdf

1.2 Windows.pdf

1. Install Python and Jupyter Notebook.html

2.1 Instructions of setting up ChatGPT.pdf

2. Setting Up ChatGPT for SMART Analysis.html

2. What is Data Science

1. Data Science and its characteristics.mp4

03:45

2. Data Science vs Data Analysis.mp4

01:56

3. Complete Data Science work-flow.mp4

03:05

4.1 Practice datasets.zip

4. Download datasets for practice and quizzes.html

5. Instructions for Quizzes IMPORTANT.html

3. Full Data Cleaning Process

1. Getting started loading your data into jupyter notebook.mp4

07:06

2. Impute missing values with Simple-Imputer.mp4

12:43

3. Rectify inconsistent variables and values.mp4

11:17

4. Identify and assign correct data types.mp4

07:43

5. Abolish duplicated data from the dataset.mp4

04:22

6. QUIZ 1 Full Data Cleaning.html

7.1 data cleaning (solution).zip

7. Solution 1 Full Data Cleaning.html

4. Full Data Manipulation Process

1. Sorting and arranging dataset.mp4

05:17

2. Conditional filtering (and, or, not etc.).mp4

10:34

3. Merging dataset with extra features.mp4

03:47

4. Concatenating data with extra data.mp4

03:47

5. QUIZ 2 Full Data Manipulation.html

6.1 data manipulation (solution).zip

6. Solution 2 Full Data Manipulation.html

5. Full Exploratory Data Analysis Process

1. Understanding exploratory data analysis.mp4

03:39

2. Investigating Value Counts Analysis Technique.mp4

22:17

3. Delving into Descriptive Statistics Analysis Technique.mp4

16:18

4. Understanding Group By Analysis Method.mp4

15:58

5. Mastering Pivot Table Analysis Method.mp4

21:59

6. Unpacking Crosstabulation Analysis Method.mp4

09:41

7. Exploring Correlation Analysis Method.mp4

05:33

8. QUIZ 3 Full Exploratory Data Analysis.html

9.1 exploratory data analysis (solution).zip

9. Solution 3 Full Exploratory Data Analysis.html

6. Understanding Statistical Data Analysis Concepts

1. Various aspects of hypothesis testing.mp4

08:08

2. Understand confidence, significance level and p-value.mp4

05:05

3. Statistical data analysis and hypothesis testing.mp4

07:40

4. QUIZ 4 Understanding Statistical Data Analysis Concepts.html

7. Various Data Transformation Techniques

1. Testing normal distribution of numeric variables.mp4

08:29

2. Square root data transformation method.mp4

06:15

3. Logarithm data transformation method.mp4

05:57

4. Box-cox data transformation method.mp4

05:45

5. Yeo-Johnson data transformation method.mp4

05:14

6. QUIZ 5 Various Data Transformation Methods.html

7.1 data transformation(solution).zip

7. Solution 5 Data Transformation Methods.html

8. Hypothesis Testing (ANOVA, Pearson Correlation, Regression)

1. One way between groups ANOVA Checking the difference.mp4

10:32

2. Pearson correlation test Checking the relationship.mp4

10:05

3. Regression test Checking the influence.mp4

14:28

4. QUIZ 6 Hypothesis Testing.html

5.1 hypothesis testing (solution).zip

5. Solution 6 Hypothesis Testing.html

9. Full Data Preprocessing Process

1. Feature engineering to generate significant variable.mp4

16:12

2. Feature encoding to assign numeric values.mp4

05:31

3. Techniques to create dummy variables.mp4

07:14

4. Feature scaling for standardization and normalization.mp4

12:19

5. Splitting data into training and testing set.mp4

06:53

6. QUIZ 7 Full Data Preprocessing.html

7.1 data preprocessing (solutions).zip

7. Solution 7 Full Data Preprocessing.html

10. Supervised Machine Learning Part 1 Regression

1. Read It IMPORTANT.html

2. Getting started Linear regression ML model.mp4

18:18

3. Decision Tree regressior ML model.mp4

08:08

4. Random Forest regressor ML model.mp4

08:04

5. Support Vector regressor ML model.mp4

06:26

6. XGBoost regressor ML model.mp4

07:37

7. QUIZ 8 Supervised ML model Part 1.html

8.1 supervised ml model part 1.zip

8. Solution 8 Supervised ML model Part 1.html

11. Supervised Machine Learning Part 2 Classification

1. Getting started Logistic regression ML model.mp4

22:35

2. Decision Tree classification ML model.mp4

13:12

3. Random Forest classification ML model.mp4

11:50

4. K Nearest Neighbours classification ML model.mp4

20:30

5. LightGBM classification ML model.mp4

13:36

6. QUIZ 9 Supervised ML model Part 2.html

7.1 supervised ml model part 2.zip

7. Solution 9 Supervised ML model Part 2.html

12. Unsupervised Machine Learning Cluster Analysis

1. KMeans clustering ML model.mp4

22:05

2. DBSCAN clustering ML model.mp4

20:19

3. Final QUIZ 10 Unsupervised ML model.html

4.1 final - all & unsupervised ml model (solutions).zip

4. Final Solution 10 Complete & Unsupervised ML model.html

13. Tips, Tricks and Resources

1. Kaggle for vast practice resources & portfolio.mp4

04:44

2. ChatGPT Your Fastest Code Companion.mp4

07:17

3.1 Complete DS Process.pptx

3.2 Confidence, significance and p value.pdf

3.3 Data Science vs Data Analysis.pptx

3.4 Data Science.pptx

3.5 Steps in Hypothesis Testing.pdf

3.6 Various aspects of Hypothesis.pdf

3. Course resources.html

Description

Master the Data Science Universe: A Comprehensive Hands-On Course Covering the A-Z of Data Science Workflow in Python

What You'll Learn?

Gain a profound understanding of the core concepts and principles of data science, including its role, importance, and applications in various industries.
Acquire the skills to clean raw data effectively, covering techniques for handling missing values, addressing different data types, and managing outliers etc.
Master data manipulation by learning essential techniques such as sorting, filtering, merging, concatenating, and others using Python's pandas library.
Learn exploratory data analysis techniques include frequencies, percentages, group-by operations, pivot tables, crosstabulation, and variable relationships.
Dive into the world of data preprocessing with hands-on experience in feature engineering, selection, and scaling to prepare datasets for ML models.
Apply your knowledge through a series of practical projects, reinforcing your understanding of each step in the data science workflow.
Develop expertise in building and evaluating supervised regression models, including linear regression, random forest, decision tree, xgboost, and more.
Gain practical skills in deploying supervised classification models, covering algorithms such as logistic regression, random forest, KNN, and lightgbm.
Explore the world of unsupervised learning by understanding and implementing clustering models like KMeans and DBSCAN for uncovering hidden patterns in data.
Become proficient in using essential Python libraries for data science, including pandas, numpy, seaborn, matplotlib, scikit-learn, and scipy.
Test your knowledge and reinforce your learning through a series of seven-layered quizzes that cover various aspects of the data science workflow.
Experience the integration of ChatGPT to rise your understanding of data science applications through interactive conversations and real-world problem-solving.
Learn how to communicate your findings effectively, translating complex data science results into clear and actionable insights for stakeholders.
Kickstart a career in data science, with a well-rounded understanding of the complete data science workflow and the ability to tackle real-world challenges.

Who is this for?

Beginners Data Scientists

Anyone Curious About Data Science

Python and Data Enthusiast

What You Need to Know?

No Coding Experience is Needed.

Desire to Learn Data Science

More details

Description
Welcome to "Data Science Crash Course in Python: A Comprehensive Hands-On Course Covering the A-Z of Data Science Workflow in Python." This transformative program is meticulously crafted to cater to diverse learners, including aspiring data scientists, professionals seeking a career transition, and anyone intrigued by the immense potential of data. Embark on a journey that demystifies the complexities of data science, providing you with not only theoretical knowledge but, more crucially, practical skills through immersive hands-on projects.
Foundations of Data Science Excellence: Delve into the fundamental principles that underpin data science. Understand its significance, applications across industries, and the pivotal role it plays in decision-making processes. From the very basics to real-world applications, this course ensures you grasp the foundations, setting the stage for a comprehensive exploration of the data science landscape.
Hands-On Data Mastery and Practical Projects: Gain a profound understanding of data manipulation, cleaning, and preprocessing through hands-on projects. Tackle real-world data challenges and reinforce your skills with seven layers of quizzes. This practical approach ensures that you not only understand the concepts but can also apply them confidently in real-world scenarios.
Model Building Excellence Across Domains: Master the art of building and evaluating both supervised regression and classification models. Dive deep into algorithms such as linear regression, random forest, logistic regression, KNN, and more. Uncover the power of unsupervised learning through clustering models like KMeans and DBSCAN, allowing you to extract valuable insights from unstructured data.
Python Libraries Demystified: Navigate the rich Python data science ecosystem effortlessly. From data manipulation using pandas to machine learning with scikit-learn, and visualization with seaborn and matplotlib, you'll gain proficiency in utilizing essential libraries that form the backbone of data science workflows.
Interactive Learning with ChatGPT Integration: Experience a unique learning journey with the integration of ChatGPT. Engage in interactive conversations, troubleshoot real-world problems, and enhance your problem-solving skills. This dynamic learning environment not only imparts knowledge but also fosters a deep understanding of the material through practical, interactive experiences.
"Data Science Crash Course in Python" goes beyond a traditional courseâ€”it's your gateway to becoming a proficient data scientist. As you unravel the intricacies of data science, you'll not only gain theoretical knowledge but also the confidence and skills needed to tackle complex challenges in the real world. Are you ready to unlock the full potential of data? Enroll now and take the first step towards a rewarding journey in data science.
Who this course is for:
Beginners Data Scientists
Anyone Curious About Data Science
Python and Data Enthusiast

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Introducing Shahriar: Accomplished Data Analyst and Passionate InstructorWelcome to the dynamic world of data analytics, where every byte of information holds the key to unlocking insights and shaping informed decisions. Meet Shahriar, a seasoned freelancer and a dedicated data enthusiast, who is now poised to share his wealth of knowledge and expertise as an esteemed Instructor at Udemy.With a rich tapestry of experiences spanning over three prolific years in the field, Shahriar has made an indelible mark as a Data Analyst extraordinaire. Over the course of his illustrious career, he has successfully spearheaded and completed an impressive portfolio of more than 300 projects, transcending geographical boundaries and leaving a trail of satisfied clients worldwide.Proficient in an array of cutting-edge tools and languages including Python, R, Excel, and SPSS, Shahriar's prowess extends across a spectrum of domains within the data realm. His unparalleled skills encompass data analysis, machine learning, data manipulation, data visualization, and applied statistics, to name just a few. His holistic approach to problem-solving ensures that every project is meticulously analyzed, curated, and executed to perfection, delivering results that are not just insightful but also actionable.However, what truly sets Shahriar apart is his unwavering passion for data. To him, data is more than just numbers and figures; it's a story waiting to be told. As an avid storyteller, he seamlessly weaves narratives from complex datasets, transforming raw information into compelling insights that resonate with both experts and novices. Shahriar's ability to make data relatable and engaging underscores his dedication to demystifying the world of data analytics.As he steps into his new role as an Instructor at Udemy, Shahriar is committed to sharing his wealth of knowledge with a global audience. His teaching philosophy is rooted in clarity, accessibility, and practicality. Whether you're a budding data enthusiast taking your first steps or a seasoned professional seeking to refine your skills, Shahriar's meticulously crafted courses are designed to empower and elevate your data prowess.In conclusion, Shahriar's journey from a passionate data hobbyist to a prolific freelancer and now an esteemed Udemy Instructor is a testament to his unwavering dedication, insatiable curiosity, and relentless pursuit of excellence in the realm of data analytics. Embark on this transformative learning journey with Shahriar and unravel the mysteries of data in a way that only a true storyteller can unveil. The world of data analytics awaits, and Shahriar is your guiding light.

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