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Data Analytics 360: Become Data Analyst in Python & Excel

سرفصل های دوره

Master Python and Excel - 2 Widely Used Tools for A-Z Data Analysis with Complete Foundations and Hands-on Applications.


1. All You Need to Know about Data Analysis
  • 1.1 1. Un DA.pdf
  • 1. Data analysis definition, types and examples
  • 2.1 2. Key COMP.pdf
  • 2. Key components of data analysis
  • 3.1 3. Tools.pdf
  • 3. Tools and technologies for data analysis
  • 4.1 4. Application DA.pdf
  • 4. Real-world application of data analysis
  • 5. Understanding data analysis.html

  • 2. Data Collection Methods and Considerations
  • 1.1 5. Data source.pdf
  • 1. Various sources of collecting data
  • 2.1 6. Sampling methods.pdf
  • 2. Population vs sample and its methods
  • 3. Understanding data collection.html

  • 3. Understand Data Cleaning and Its Methods
  • 1.1 8. Data Cleaning.pdf
  • 1. Why you cannot ignore cleaning your data
  • 2.1 9. Methods of DC.pdf
  • 2. Various aspects of data cleaning
  • 3. Techniques of Data Cleaning.html

  • 4. Explore Joining and Concatenating Methods
  • 1.1 20. Joining.pdf
  • 1. Various aspects of Joining datasets
  • 2.1 21. Concatenating.pdf
  • 2. Adding extra data with concatenation
  • 3. Understanding joining and concatenation.html

  • 5. Complete Picture of Exploratory Data Analysis
  • 1.1 11. EDA.pdf
  • 1. EDA for generating significant insights
  • 2. Methods of exploratory data analysis Part 1
  • 3. Methods of exploratory data analysis Part 2
  • 4.1 12. 13. 14. Methods of EDA.pdf
  • 4. Methods of exploratory data analysis Part 3
  • 5. Exploratory Data Analysis.html

  • 6. Everything about Statistical Data Analysis
  • 1.1 22. Statistical Data Analysis.pdf
  • 1. The application of statistical test
  • 2.1 23. Types of Statistics.pdf
  • 2. Types of statistical data analysis
  • 3.1 24. stat v s eda.pdf
  • 3. Statistical test vs Exploratory data analysis
  • 4. A Recap on descriptive statistics methods
  • 5. Inferential statistics Part 1 T-tests and ANOVA
  • 6.1 25. 26. 27. methods of ds and is.pdf
  • 6. Inferential statistics Part 2 Relationships measures
  • 7.1 28. Lin reg.pdf
  • 7. Inferential statistics Part 3 Linear regression
  • 8. Statistical data analysis.html

  • 7. Concepts of Probabilities in Data Analysis
  • 1.1 31. Probability in Data Analysis.pdf
  • 1. Probability in data analysis
  • 2.1 32. Classical Probability.pdf
  • 2. Classical probability
  • 3.1 33. Empirical Probability.pdf
  • 3. Empirical probability
  • 4.1 34. Conditional Probability.pdf
  • 4. Conditional probability
  • 5.1 35. Joint Probability.pdf
  • 5. Joint probability
  • 6. Probabilities in data analysis.html

  • 8. Hypothesis Testing in Statistical Analysis
  • 1.1 36. Hypothesis Testing.pdf
  • 1. Hypothesis testing for inferential statistics
  • 2.1 37. Selecting Appropriate Statistical Test.pdf
  • 2. Selecting statistical test and assumption testing
  • 3.1 38. CSP.pdf
  • 3. Confidence level, significance level, p-value
  • 4.1 39. Making Decision and Conclusion.pdf
  • 4. Making decision and conclusion on findings
  • 5.1 40. Example HT.pdf
  • 5. Complete statistical analysis and hypothesis testing
  • 6. Hypothesis Testing in Statistical Analysis.html

  • 9. Explore Data Transformation and Its Methods
  • 1.1 16. DTP.pdf
  • 1. Transforming data for improved analysis
  • 2. Techniques for data transformation Part 1
  • 3.1 17. 18. Mthods of DTP.pdf
  • 3. Techniques for data transformation Part 2
  • 4. Understanding Data Transformation.html

  • 10. Machine Learning for Predictive Efficiency
  • 1.1 41. ML in Data Analysis.pdf
  • 1. ML for data analysis and decision-making
  • 2.1 42. Types of Machine Learning.pdf
  • 2. Widely used ML methods in the data analytics
  • 3.1 43. steps in ML.pdf
  • 3. Steps in developing machine learning model
  • 4. Machine learning in Data analysis.html

  • 11. Explore Data Visualizations and Its Methods
  • 1.1 44. Data Visualization.pdf
  • 1. Visualizing data for the best insight delivery
  • 2. Several methods of data visualization Part 1
  • 3. Several methods of data visualization Part 2
  • 4.1 45. 46. 47. Data Visualization Methods.pdf
  • 4. Several methods of data visualization Part 3
  • 5. Data visualization and methods.html

  • 12. Excel - Data Cleaning and Formatting
  • 1.1 employee dataset.xlsx
  • 1. Identifying and removing duplicates
  • 2. Dealing with duplicates in Excel.html
  • 3.1 missing mployee dataset.xlsx
  • 3. Dealing with missing values
  • 4. Dealing with missing values in Excel.html
  • 5.1 outliers sales data.xlsx
  • 5. Dealing with outliers
  • 6. Dealing with outliers in Excel.html
  • 7.1 Incon sales data.xlsx
  • 7. Finding and imputing inconsistent values
  • 8. Dealing with inconsistent value in Excel.html
  • 9.1 practice text to column.xlsx
  • 9. Text-to-columns for data separation
  • 10. Data separation in Excel.html

  • 13. Excel - Data Sorting and Filtering
  • 1.1 filtering data.xlsx
  • 1. Applying sorts & filters to narrow down data
  • 2. Sorting and filtering in Excel.html
  • 3.1 advanced filtering data.xlsx
  • 3. Advanced filtering with custom criteria
  • 4. Advanced filtering in Excel.html

  • 14. Excel - Apply Conditional Formatting
  • 1.1 highlight cells.xlsx
  • 1. Highlighting cells based on criteria
  • 2. Highlighting cells in Excel.html
  • 3.1 topbottom.xlsx
  • 3. Findings top and bottom insights
  • 4. Top and bottom insights in Excel.html
  • 5.1 colorbar.xlsx
  • 5. Creating color scales and color bars
  • 6. Color bar presentation in Excel.html

  • 15. Excel - Formulas and Functions for Data Analysis
  • 1.1 business growth data.xlsx
  • 1. SUM, AVERAGE, MIN, and MAX functions
  • 2. Applying SUM and AVERAGE in Excel.html
  • 3.1 business growth data.xlsx
  • 3. SUMIF, and AVERAGEIF functions
  • 4. Applying conditional aggregate function in Excel.html
  • 5.1 business growth data.xlsx
  • 5. COUNT, COUNTA, and COUNTIF functions
  • 6. Using COUNTIF function in Excel.html
  • 7.1 business growth data.xlsx
  • 7. YEAR, MONTH and DAY for date manipulation
  • 8. Extracting key elements of date in Excel.html
  • 9.1 business growth data.xlsx
  • 9. IF STATEMENTs for conditional operation
  • 10. Performing NESTED IF operation in Excel.html
  • 11.1 employeeInfo.xlsx
  • 11. VLOOKUP for column-wise insight search
  • 12. Performing VLOOKUP operation in Excel.html
  • 13.1 sales crosstab.xlsx
  • 13. HLOOKUP for row-wise insight search
  • 14. Performing HLOOKUP operation in Excel.html
  • 15.1 xlookup.xlsx
  • 15. XLOOKUP for robust & complex insight search
  • 16. Performing XLOOKUP operation in Excel.html

  • 16. Excel - Graphs and Charts for Data Visualization
  • 1.1 sales data.xlsx
  • 1. Analyze data with Stacked and cluster bar charts
  • 2. Stacked bar chart for analysis in Excel.html
  • 3.1 sales data.xlsx
  • 3. Analyze data with Pie chart and line chart
  • 4. Pie chart for analysis in Excel.html
  • 5.1 sales data.xlsx
  • 5. Analyze data with Area chart and TreeMap
  • 6. Area chart for analysis in Excel.html
  • 7.1 sales data.xlsx
  • 7. Analyze data with Boxplot and Histogram
  • 8. Boxplot for analysis in Excel.html
  • 9.1 sale data.xlsx
  • 9. Analyze data with Scatter plot and Combo chart
  • 10. Scatter plot for analysis in Excel.html
  • 11.1 sale data.xlsx
  • 11. Adjusting and decorating graphs and charts

  • 17. Excel - Data Analysis in PivotTables and PivotCharts
  • 1.1 Pivot data p1.xlsx
  • 1. PivotTables for GROUP data analysis PART 1
  • 2. PivotTables for analysis in Excel.html
  • 3.1 Pivot data p2.xlsx
  • 3. PivotTables for CROSSTAB data analysis PART 2
  • 4. PivotTables for analysis in Excel.html
  • 5.1 Pivot data p3.xlsx
  • 5. PivotCharts and Slicers for interactivity
  • 6. PivotCharts and Slicers for analysis in Excel.html

  • 18. Excel - Data Analysis ToolPack for Statistical Analysis
  • 1.1 data for statistics.xlsx
  • 1. Descriptive statistics and analysis
  • 2. Find the key descriptives of numeric data.html
  • 3.1 data for ind t-tests.xlsx
  • 3. Independent sample t-test for two samples
  • 4. Find the difference between two groups.html
  • 5.1 data for paired t-tests.xlsx
  • 5. Paired sample t-test for two samples
  • 6. Find the difference between two time frames.html
  • 7.1 data for anova.xlsx
  • 7. Analysis of variance One way ANOVA
  • 8. Find the difference among various groups.html
  • 9.1 data for corr.xlsx
  • 9. Correlation analysis for relationship
  • 10. Find the relationship of two numeric data.html
  • 11.1 data for reg.xlsx
  • 11. Multiple linear regression analysis
  • 12. Find the influence of IVs on DV.html

  • 19. Excel - Creating Interactive Dashboard
  • 1.1 1. acc inf.xlsx
  • 1. Accumulating relevant information
  • 2.1 2. canvas.xlsx
  • 2. Creating a canvas for dashboard
  • 3.1 3. dashboard.xlsx
  • 3. Developing the complete dashboard
  • 4.1 4. decor dash.xlsx
  • 4. Final touch up for dashboard decoration
  • 5. Creating a dashboard in Excel.html

  • 20. Project 1 - Bank Churn Data Analysis
  • 1. Bank Churn Data Analysis.html

  • 21. Setting Up Python and Jupyter Notebook
  • 1.1 Mac.pdf
  • 1. Installing Python and Jupyter Notebook Mac.html
  • 2.1 Windows.pdf
  • 2. Installing Python and Jupyter Notebook Windows.html
  • 3. More alternative methods Check the article.html
  • 4.1 Resources.zip
  • 4. Resources used for this section.html

  • 22. Python - Starting with Variables to Data Types
  • 1. Getting started with first python code
  • 2. Printing function.html
  • 3. Assigning variable names correctly
  • 4. Creating variables.html
  • 5. Various data types and data structures
  • 6. Converting and casting data types
  • 7. Converting data types #1.html
  • 8. Converting data types #2.html
  • 9. Converting data types #3.html
  • 10.1 starting with variables to data types.zip
  • 10. Starting with Variables to Data Types.html

  • 23. Python - Operators in Python Programming
  • 1. Arithmetic operators (+, -, , , %, )
  • 2. Arithmetic operation #1.html
  • 3. Arithmetic operation #2.html
  • 4. Arithmetic operation #3.html
  • 5. Arithmetic operation #4.html
  • 6. Arithmetic operation #5.html
  • 7. Arithmetic operation #6.html
  • 8. Comparison operators (, , =, =, ==, !=)
  • 9. Comparison operation #1.html
  • 10. Comparison operation #2.html
  • 11. Comparison operation #3.html
  • 12. Comparison operation #4.html
  • 13. Logical operators (and, or, not)
  • 14.1 operators in python programming.zip
  • 14. Operators in Python Programming.html

  • 24. Python - Dealing with Data Structures
  • 1. Lists creation, indexing, slicing, modifying
  • 2. Creating list.html
  • 3. Indexing list.html
  • 4. Slicing list.html
  • 5. Adding element.html
  • 6. Removing element.html
  • 7. Replacing element.html
  • 8. Sets unique elements, operations
  • 9. Union sets.html
  • 10. Reducing sets.html
  • 11. Dictionaries key-value pairs, methods
  • 12. Create dictionary.html
  • 13. Adding keys and values.html
  • 14.1 dealing with data structures.zip
  • 14. Several data structures.html

  • 25. Python - Conditionals Looping and Functions
  • 1. Conditional statements (if, elif, else)
  • 2. Conditional statement #1.html
  • 3. Conditional statement #2.html
  • 4. Nested logical expressions in conditions
  • 5. Logical expression #1.html
  • 6. Logical expression #2.html
  • 7. Logical expression #3.html
  • 8. Looping structures (for loops, while loops)
  • 9. For loop.html
  • 10. While loop.html
  • 11. Defining, creating, and calling functions
  • 12. Dealing with function #1.html
  • 13. Dealing with function #2.html
  • 14.1 conditionals looping and functions.zip
  • 14. Conditionals Looping and Functions.html

  • 26. Python - Sequential Cleaning and Modifying Data
  • 1.1 1. loading data.pdf
  • 1. Preparing notebook and loading data
  • 2. Loading csv data.html
  • 3.1 2. identify missing values.pdf
  • 3. Identifying missing or null values
  • 4. missing values.html
  • 5.1 3. imputing missing values.pdf
  • 5. Method of missing value imputation
  • 6. imputing missing values.html
  • 7.1 4. checking data types.pdf
  • 7. Exploring data types in a dataframe
  • 8. Checking data types.html
  • 9.1 5. removing inconsistent value.pdf
  • 9. Dealing with inconsistent values
  • 10. Finding the unique values.html
  • 11. Removing inconsistent value.html
  • 12.1 6. assigning data type.pdf
  • 12. Assigning correct data types
  • 13. Assigning data type.html
  • 14.1 7. dealing with duplicates.pdf
  • 14. Dealing with duplicated values
  • 15. Identify duplicates.html
  • 16. Removing duplicates.html
  • 17.1 data loading and cleaning .zip
  • 17. Sequential data cleaning and modifying.html

  • 27. Python - Various Methods of Data Manipulation
  • 1.1 8. sorting data.pdf
  • 1. Sorting data by column and order
  • 2. dataset sorting.html
  • 3.1 9. boolean filtering.pdf
  • 3. Filtering data with boolean indexing
  • 4. Boolean filtering #1.html
  • 5. Boolean filtering #2.html
  • 6.1 10. query.pdf
  • 6. Query method for precise filtering
  • 7. Query method.html
  • 8.1 11. is in.pdf
  • 8. Filtering data with isin method
  • 9. IsIn filtering method.html
  • 10.1 12. loc and iloc.pdf
  • 10. Slicing dataframe with loc and iloc
  • 11. Slicing with loc.html
  • 12. Slicing with iloc.html
  • 13.1 13. combining conditions.pdf
  • 13. Filtering data for many conditions
  • 14. Multiple conditions.html
  • 15.1 data sorting and filtering.zip
  • 15. Various methods of data manipulation.html

  • 28. Python - Merging and Concatenating Dataframes
  • 1.1 14. joining data.pdf
  • 1. Joining dataframes horizontally
  • 2. Inner joining.html
  • 3.1 15. concatenating data.pdf
  • 3. Concatenate dataframes vertically
  • 4. Vertical concatenation.html
  • 5.1 merging and joining dataframes.zip
  • 5. Merging and joining dataframes.html

  • 29. Python - Applied Exploratory Data Analysis Methods
  • 1.1 16. value counts.pdf
  • 1. Frequency and percentage analysis
  • 2. Value counts method.html
  • 3.1 17. descriptive.pdf
  • 3. Descriptive statistics and analysis
  • 4. Descriptive statistics.html
  • 5.1 18. group by.pdf
  • 5. Group by data analysis method
  • 6. Group by method.html
  • 7.1 19. pivot table.pdf
  • 7. Pivot table analysis - all in one
  • 8. Pivot table.html
  • 9.1 20. crosstab.pdf
  • 9. Cross-tabulation analysis method
  • 10. Cross-tabulation.html
  • 11.1 21. correl.pdf
  • 11. Correlation analysis for numeric data
  • 12. Correlation analysis.html
  • 13.1 applied exploratory data analysis.zip
  • 13. Applied exploratory data analysis.html

  • 30. Python - Exploring Data Visualisations Methods
  • 1.1 22. methods used in visualisation.pdf
  • 1. Understanding visualisation tools
  • 2.1 23. bar chart.pdf
  • 2. Getting started with bar charts
  • 3. Bar chart.html
  • 4.1 24. stacked or clustered .pdf
  • 4. Stacked and clustered bar charts
  • 5. Clustered bar plot.html
  • 6.1 25. pie chart.pdf
  • 6. Pie chart for percentage analysis
  • 7. Pie chart.html
  • 8.1 26. line plot.pdf
  • 8. Line chart for grouping data analysis
  • 9. Line chart.html
  • 10.1 27. histogram.pdf
  • 10. Exploring distribution with histogram
  • 11. Histogram.html
  • 12.1 28. scatterplot.pdf
  • 12. Correlation analysis via scatterplot
  • 13. Scatter plot.html
  • 14.1 29. heatmap.pdf
  • 14. Matrix visualisation with heatmap
  • 15. Heatmap.html
  • 16.1 30. boxplot.pdf
  • 16. Boxplot statistical visualisation method
  • 17. Box plot.html
  • 18.1 data visualisations.zip
  • 18. Exploring data visualisations methods.html

  • 31. Python - Practical Data Transformation Methods
  • 1.1 31. check distribution.pdf
  • 1. Investigating distribution of numeric data
  • 2. Kdeplot for distribution.html
  • 3.1 32. normality test.pdf
  • 3. Shapiro Wilk test of normality
  • 4. Normality test.html
  • 5.1 33. square root transformation.pdf
  • 5. Starting with square root transformation
  • 6. SQRT transformation.html
  • 7.1 34. log transformation.pdf
  • 7. Logarithmic transformation method
  • 8. LOG transformation.html
  • 9.1 35. boxcox transformation.pdf
  • 9. Box-cox power transformation method
  • 10. BOXCOX transformation.html
  • 11.1 36. yeojohnson transformation.pdf
  • 11. Yeo-Johnson power transformation method
  • 12. YEO-JOHNSON transformation.html
  • 13.1 transformation methods.zip
  • 13. Practical data transformation methods.html

  • 32. Python - Statistical Tests and Hypothesis Testing
  • 1.1 37. one sample ttest.pdf
  • 1. One sample t-test
  • 2. One sample t-test.html
  • 3.1 38. independent sample t-test.pdf
  • 3. Independent sample t-test
  • 4. Two sample t-test.html
  • 5.1 39. one way anova.pdf
  • 5. One way Analysis of Variance
  • 6. Levenes test.html
  • 7. Analysis of variance.html
  • 8.1 40. chi test for ind.pdf
  • 8. Chi square test for independence
  • 9. Cross-tabulation test.html
  • 10.1 41. pearson correlation.pdf
  • 10. Pearson correlation analysis
  • 11. Pearson correlation.html
  • 12.1 42. linear regression.pdf
  • 12. Linear regression analysis
  • 13. Linear regression test.html
  • 14.1 statistical tests and hypothesis testing.zip
  • 14. Statistical tests and hypothesis testing.html

  • 33. Python - Exploring Feature Engineering Methods
  • 1.1 43. feature generation.pdf
  • 1. Generating new features
  • 2. Feature generation.html
  • 3.1 44. datetime data.pdf
  • 3. Extracting day, month and year
  • 4. Date element extraction.html
  • 5.1 45. feature encoding.pdf
  • 5. Encoding features - LabelEncoder
  • 6. Feature encoding.html
  • 7.1 46. feature binning.pdf
  • 7. Categorizing numeric feature
  • 8. Feature binning.html
  • 9.1 46. feature mapping.pdf
  • 9. Manual feature encoding
  • 10. Feature mapping.html
  • 11.1 47. creating dummies.pdf
  • 11. Converting features into dummy
  • 12. Generating dummies.html
  • 13.1 feature engineering methods.zip
  • 13. Feature engineering methods.html

  • 34. Python - Data Preprocessing for Machine Learning
  • 1.1 48. selecting features.pdf
  • 1. Selecting features and target
  • 2. Feature selection.html
  • 3.1 49. standard scaling.pdf
  • 3. Scaling features - StandardScaler
  • 4. Standard scaling.html
  • 5.1 50. minmax scaler.pdf
  • 5. Scaling features - MinMaxScaler
  • 6. MinMax scaling.html
  • 7.1 51. PCA.pdf
  • 7. Dimensionality reduction with PCA
  • 8. Explained variance ratio.html
  • 9. Select n component.html
  • 10. Principal component analysis.html
  • 11.1 52. train test split.pdf
  • 11. Splitting into train and test set
  • 12. Train test split.html
  • 13.1 preprocessing for machine learning.zip
  • 13. Preprocessing for machine learning.html

  • 35. Python - Supervised Regression ML Models
  • 1.1 1. LR model ML.pdf
  • 1. Linear regression ML model
  • 2. Build Linear Regression ML.html
  • 3. Make prediction with LR model.html
  • 4. Evaluate the LR model.html
  • 5.1 2. DTR model ML.pdf
  • 5. Decision tree regressor ML model
  • 6. Decision tree regressor.html
  • 7.1 3. RFR model ML.pdf
  • 7. Random forest regressor ML model
  • 8. Random forest regressor.html
  • 9.1 regression machine learning.zip
  • 9. Supervised regression ML models.html

  • 36. Python - Supervised Classification ML Models
  • 1.1 4. LGR model ML.pdf
  • 1. Logistic regression ML model
  • 2. Build Logistic Regression ML.html
  • 3. Evaluate the LGR model.html
  • 4.1 5. DTC model ML.pdf
  • 4. Decision tree classification ML model
  • 5. Decision tree classification.html
  • 6.1 6. RFC model ML.pdf
  • 6. Random forest classification ML model
  • 7. Random forest classification.html
  • 8.1 classification machine learning.zip
  • 8. Supervised classification ML models.html

  • 37. Python - Segmentation with KMeans Clustering
  • 1. Calculating within cluster sum of squares
  • 2. Calculating WCSS.html
  • 3.1 7. selecting best k.pdf
  • 3. Selecting optimal number of clusters
  • 4. Plotting Elbow chart.html
  • 5.1 8. developing k means.pdf
  • 5. Application of KMeans machine learning
  • 6. Building KMeans cluster.html
  • 7.1 data segmentation with kmeans clustering.zip
  • 7. Data segmentation with KMeans clustering.html

  • 38. Project 2 - Sports Data Analytics
  • 1. Segmenting and Classifying the Best Strikers.html

  • 39. Resources - Python and Excel
  • 1.1 Microsoft Excel Data Analysis Cheat Sheet.pdf
  • 1. Extra note on functions and shortcuts.html
  • 2.1 Python data analysis code cheatsheet.pdf
  • 2. Extra note on python data analysis.html
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