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

سرفصل های دوره

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


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
  • 2. Data Science vs Data Analysis
  • 3. Complete Data Science work-flow
  • 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
  • 2. Impute missing values with Simple-Imputer
  • 3. Rectify inconsistent variables and values
  • 4. Identify and assign correct data types
  • 5. Abolish duplicated data from the dataset
  • 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
  • 2. Conditional filtering (and, or, not etc.)
  • 3. Merging dataset with extra features
  • 4. Concatenating data with extra data
  • 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
  • 2. Investigating Value Counts Analysis Technique
  • 3. Delving into Descriptive Statistics Analysis Technique
  • 4. Understanding Group By Analysis Method
  • 5. Mastering Pivot Table Analysis Method
  • 6. Unpacking Crosstabulation Analysis Method
  • 7. Exploring Correlation Analysis Method
  • 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
  • 2. Understand confidence, significance level and p-value
  • 3. Statistical data analysis and hypothesis testing
  • 4. QUIZ 4 Understanding Statistical Data Analysis Concepts.html

  • 7. Various Data Transformation Techniques
  • 1. Testing normal distribution of numeric variables
  • 2. Square root data transformation method
  • 3. Logarithm data transformation method
  • 4. Box-cox data transformation method
  • 5. Yeo-Johnson data transformation method
  • 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
  • 2. Pearson correlation test Checking the relationship
  • 3. Regression test Checking the influence
  • 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
  • 2. Feature encoding to assign numeric values
  • 3. Techniques to create dummy variables
  • 4. Feature scaling for standardization and normalization
  • 5. Splitting data into training and testing set
  • 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
  • 3. Decision Tree regressior ML model
  • 4. Random Forest regressor ML model
  • 5. Support Vector regressor ML model
  • 6. XGBoost regressor ML model
  • 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
  • 2. Decision Tree classification ML model
  • 3. Random Forest classification ML model
  • 4. K Nearest Neighbours classification ML model
  • 5. LightGBM classification ML model
  • 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
  • 2. DBSCAN clustering ML model
  • 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
  • 2. ChatGPT Your Fastest Code Companion
  • 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
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    تاریخ انتشار: ۲۷ آذر ۱۴۰۲
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