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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