Data Analytics 360: Become Data Analyst in Python & Excel

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1. All You Need to Know about Data Analysis

1.1 1. Un DA.pdf

1. Data analysis definition, types and examples.mp4

07:11

2.1 2. Key COMP.pdf

2. Key components of data analysis.mp4

10:18

3.1 3. Tools.pdf

3. Tools and technologies for data analysis.mp4

08:41

4.1 4. Application DA.pdf

4. Real-world application of data analysis.mp4

04:25

5. Understanding data analysis.html

2. Data Collection Methods and Considerations

1.1 5. Data source.pdf

1. Various sources of collecting data.mp4

05:27

2.1 6. Sampling methods.pdf

2. Population vs sample and its methods.mp4

11:05

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.mp4

04:04

2.1 9. Methods of DC.pdf

2. Various aspects of data cleaning.mp4

14:12

3. Techniques of Data Cleaning.html

4. Explore Joining and Concatenating Methods

1.1 20. Joining.pdf

1. Various aspects of Joining datasets.mp4

08:50

2.1 21. Concatenating.pdf

2. Adding extra data with concatenation.mp4

04:05

3. Understanding joining and concatenation.html

5. Complete Picture of Exploratory Data Analysis

1.1 11. EDA.pdf

1. EDA for generating significant insights.mp4

05:37

2. Methods of exploratory data analysis Part 1.mp4

12:15

3. Methods of exploratory data analysis Part 2.mp4

11:17

4.1 12. 13. 14. Methods of EDA.pdf

4. Methods of exploratory data analysis Part 3.mp4

15:36

5. Exploratory Data Analysis.html

6. Everything about Statistical Data Analysis

1.1 22. Statistical Data Analysis.pdf

1. The application of statistical test.mp4

08:31

2.1 23. Types of Statistics.pdf

2. Types of statistical data analysis.mp4

04:58

3.1 24. stat v s eda.pdf

3. Statistical test vs Exploratory data analysis.mp4

04:10

4. A Recap on descriptive statistics methods.mp4

03:14

5. Inferential statistics Part 1 T-tests and ANOVA.mp4

06:54

6.1 25. 26. 27. methods of ds and is.pdf

6. Inferential statistics Part 2 Relationships measures.mp4

03:16

7.1 28. Lin reg.pdf

7. Inferential statistics Part 3 Linear regression.mp4

11:53

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.mp4

08:10

2.1 32. Classical Probability.pdf

2. Classical probability.mp4

04:58

3.1 33. Empirical Probability.pdf

3. Empirical probability.mp4

05:21

4.1 34. Conditional Probability.pdf

4. Conditional probability.mp4

07:07

5.1 35. Joint Probability.pdf

5. Joint probability.mp4

04:47

6. Probabilities in data analysis.html

8. Hypothesis Testing in Statistical Analysis

1.1 36. Hypothesis Testing.pdf

1. Hypothesis testing for inferential statistics.mp4

06:03

2.1 37. Selecting Appropriate Statistical Test.pdf

2. Selecting statistical test and assumption testing.mp4

10:26

3.1 38. CSP.pdf

3. Confidence level, significance level, p-value.mp4

03:37

4.1 39. Making Decision and Conclusion.pdf

4. Making decision and conclusion on findings.mp4

02:55

5.1 40. Example HT.pdf

5. Complete statistical analysis and hypothesis testing.mp4

07:01

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.mp4

05:14

2. Techniques for data transformation Part 1.mp4

06:11

3.1 17. 18. Mthods of DTP.pdf

3. Techniques for data transformation Part 2.mp4

04:07

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.mp4

06:07

2.1 42. Types of Machine Learning.pdf

2. Widely used ML methods in the data analytics.mp4

11:11

3.1 43. steps in ML.pdf

3. Steps in developing machine learning model.mp4

07:43

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.mp4

04:33

2. Several methods of data visualization Part 1.mp4

04:04

3. Several methods of data visualization Part 2.mp4

05:38

4.1 45. 46. 47. Data Visualization Methods.pdf

4. Several methods of data visualization Part 3.mp4

07:17

5. Data visualization and methods.html

12. Excel - Data Cleaning and Formatting

1.1 employee dataset.xlsx

1. Identifying and removing duplicates.mp4

04:52

2. Dealing with duplicates in Excel.html

3.1 missing mployee dataset.xlsx

3. Dealing with missing values.mp4

14:55

4. Dealing with missing values in Excel.html

5.1 outliers sales data.xlsx

5. Dealing with outliers.mp4

09:50

6. Dealing with outliers in Excel.html

7.1 Incon sales data.xlsx

7. Finding and imputing inconsistent values.mp4

07:12

8. Dealing with inconsistent value in Excel.html

9.1 practice text to column.xlsx

9. Text-to-columns for data separation.mp4

03:28

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.mp4

07:26

2. Sorting and filtering in Excel.html

3.1 advanced filtering data.xlsx

3. Advanced filtering with custom criteria.mp4

05:59

4. Advanced filtering in Excel.html

14. Excel - Apply Conditional Formatting

1.1 highlight cells.xlsx

1. Highlighting cells based on criteria.mp4

08:24

2. Highlighting cells in Excel.html

3.1 topbottom.xlsx

3. Findings top and bottom insights.mp4

04:44

4. Top and bottom insights in Excel.html

5.1 colorbar.xlsx

5. Creating color scales and color bars.mp4

06:52

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.mp4

09:42

2. Applying SUM and AVERAGE in Excel.html

3.1 business growth data.xlsx

3. SUMIF, and AVERAGEIF functions.mp4

08:32

4. Applying conditional aggregate function in Excel.html

5.1 business growth data.xlsx

5. COUNT, COUNTA, and COUNTIF functions.mp4

08:04

6. Using COUNTIF function in Excel.html

7.1 business growth data.xlsx

7. YEAR, MONTH and DAY for date manipulation.mp4

03:41

8. Extracting key elements of date in Excel.html

9.1 business growth data.xlsx

9. IF STATEMENTs for conditional operation.mp4

10:42

10. Performing NESTED IF operation in Excel.html

11.1 employeeInfo.xlsx

11. VLOOKUP for column-wise insight search.mp4

06:56

12. Performing VLOOKUP operation in Excel.html

13.1 sales crosstab.xlsx

13. HLOOKUP for row-wise insight search.mp4

06:59

14. Performing HLOOKUP operation in Excel.html

15.1 xlookup.xlsx

15. XLOOKUP for robust & complex insight search.mp4

07:05

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.mp4

12:43

2. Stacked bar chart for analysis in Excel.html

3.1 sales data.xlsx

3. Analyze data with Pie chart and line chart.mp4

08:32

4. Pie chart for analysis in Excel.html

5.1 sales data.xlsx

5. Analyze data with Area chart and TreeMap.mp4

08:29

6. Area chart for analysis in Excel.html

7.1 sales data.xlsx

7. Analyze data with Boxplot and Histogram.mp4

05:14

8. Boxplot for analysis in Excel.html

9.1 sale data.xlsx

9. Analyze data with Scatter plot and Combo chart.mp4

04:57

10. Scatter plot for analysis in Excel.html

11.1 sale data.xlsx

11. Adjusting and decorating graphs and charts.mp4

04:47

17. Excel - Data Analysis in PivotTables and PivotCharts

1.1 Pivot data p1.xlsx

1. PivotTables for GROUP data analysis PART 1.mp4

09:16

2. PivotTables for analysis in Excel.html

3.1 Pivot data p2.xlsx

3. PivotTables for CROSSTAB data analysis PART 2.mp4

05:06

4. PivotTables for analysis in Excel.html

5.1 Pivot data p3.xlsx

5. PivotCharts and Slicers for interactivity.mp4

06:41

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.mp4

06:12

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.mp4

08:41

4. Find the difference between two groups.html

5.1 data for paired t-tests.xlsx

5. Paired sample t-test for two samples.mp4

05:08

6. Find the difference between two time frames.html

7.1 data for anova.xlsx

7. Analysis of variance One way ANOVA.mp4

07:30

8. Find the difference among various groups.html

9.1 data for corr.xlsx

9. Correlation analysis for relationship.mp4

10:16

10. Find the relationship of two numeric data.html

11.1 data for reg.xlsx

11. Multiple linear regression analysis.mp4

12:23

12. Find the influence of IVs on DV.html

19. Excel - Creating Interactive Dashboard

1.1 1. acc inf.xlsx

1. Accumulating relevant information.mp4

08:40

2.1 2. canvas.xlsx

2. Creating a canvas for dashboard.mp4

05:32

3.1 3. dashboard.xlsx

3. Developing the complete dashboard.mp4

07:36

4.1 4. decor dash.xlsx

4. Final touch up for dashboard decoration.mp4

05:09

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.mp4

04:36

2. Printing function.html

3. Assigning variable names correctly.mp4

09:05

4. Creating variables.html

5. Various data types and data structures.mp4

07:22

6. Converting and casting data types.mp4

09:49

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 (+, -, , , %, ).mp4

06:44

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 (, , =, =, ==, !=).mp4

07:35

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).mp4

07:06

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.mp4

15:59

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.mp4

07:51

9. Union sets.html

10. Reducing sets.html

11. Dictionaries key-value pairs, methods.mp4

09:23

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).mp4

07:04

2. Conditional statement #1.html

3. Conditional statement #2.html

4. Nested logical expressions in conditions.mp4

09:57

5. Logical expression #1.html

6. Logical expression #2.html

7. Logical expression #3.html

8. Looping structures (for loops, while loops).mp4

09:12

9. For loop.html

10. While loop.html

11. Defining, creating, and calling functions.mp4

05:11

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.mp4

13:14

2. Loading csv data.html

3.1 2. identify missing values.pdf

3. Identifying missing or null values.mp4

05:02

4. missing values.html

5.1 3. imputing missing values.pdf

5. Method of missing value imputation.mp4

13:23

6. imputing missing values.html

7.1 4. checking data types.pdf

7. Exploring data types in a dataframe.mp4

05:37

8. Checking data types.html

9.1 5. removing inconsistent value.pdf

9. Dealing with inconsistent values.mp4

08:33

10. Finding the unique values.html

11. Removing inconsistent value.html

12.1 6. assigning data type.pdf

12. Assigning correct data types.mp4

04:33

13. Assigning data type.html

14.1 7. dealing with duplicates.pdf

14. Dealing with duplicated values.mp4

05:20

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.mp4

05:51

2. dataset sorting.html

3.1 9. boolean filtering.pdf

3. Filtering data with boolean indexing.mp4

08:47

4. Boolean filtering #1.html

5. Boolean filtering #2.html

6.1 10. query.pdf

6. Query method for precise filtering.mp4

05:59

7. Query method.html

8.1 11. is in.pdf

8. Filtering data with isin method.mp4

05:37

9. IsIn filtering method.html

10.1 12. loc and iloc.pdf

10. Slicing dataframe with loc and iloc.mp4

10:21

11. Slicing with loc.html

12. Slicing with iloc.html

13.1 13. combining conditions.pdf

13. Filtering data for many conditions.mp4

07:19

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.mp4

07:26

2. Inner joining.html

3.1 15. concatenating data.pdf

3. Concatenate dataframes vertically.mp4

08:33

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.mp4

06:56

2. Value counts method.html

3.1 17. descriptive.pdf

3. Descriptive statistics and analysis.mp4

09:03

4. Descriptive statistics.html

5.1 18. group by.pdf

5. Group by data analysis method.mp4

05:41

6. Group by method.html

7.1 19. pivot table.pdf

7. Pivot table analysis - all in one.mp4

13:42

8. Pivot table.html

9.1 20. crosstab.pdf

9. Cross-tabulation analysis method.mp4

04:42

10. Cross-tabulation.html

11.1 21. correl.pdf

11. Correlation analysis for numeric data.mp4

08:37

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.mp4

06:46

2.1 23. bar chart.pdf

2. Getting started with bar charts.mp4

12:18

3. Bar chart.html

4.1 24. stacked or clustered .pdf

4. Stacked and clustered bar charts.mp4

09:40

5. Clustered bar plot.html

6.1 25. pie chart.pdf

6. Pie chart for percentage analysis.mp4

06:23

7. Pie chart.html

8.1 26. line plot.pdf

8. Line chart for grouping data analysis.mp4

07:53

9. Line chart.html

10.1 27. histogram.pdf

10. Exploring distribution with histogram.mp4

10:30

11. Histogram.html

12.1 28. scatterplot.pdf

12. Correlation analysis via scatterplot.mp4

09:03

13. Scatter plot.html

14.1 29. heatmap.pdf

14. Matrix visualisation with heatmap.mp4

08:01

15. Heatmap.html

16.1 30. boxplot.pdf

16. Boxplot statistical visualisation method.mp4

08:40

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.mp4

21:31

2. Kdeplot for distribution.html

3.1 32. normality test.pdf

3. Shapiro Wilk test of normality.mp4

10:57

4. Normality test.html

5.1 33. square root transformation.pdf

5. Starting with square root transformation.mp4

14:05

6. SQRT transformation.html

7.1 34. log transformation.pdf

7. Logarithmic transformation method.mp4

10:18

8. LOG transformation.html

9.1 35. boxcox transformation.pdf

9. Box-cox power transformation method.mp4

08:51

10. BOXCOX transformation.html

11.1 36. yeojohnson transformation.pdf

11. Yeo-Johnson power transformation method.mp4

09:34

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.mp4

09:56

2. One sample t-test.html

3.1 38. independent sample t-test.pdf

3. Independent sample t-test.mp4

12:02

4. Two sample t-test.html

5.1 39. one way anova.pdf

5. One way Analysis of Variance.mp4

14:55

6. Levenes test.html

7. Analysis of variance.html

8.1 40. chi test for ind.pdf

8. Chi square test for independence.mp4

07:18

9. Cross-tabulation test.html

10.1 41. pearson correlation.pdf

10. Pearson correlation analysis.mp4

10:28

11. Pearson correlation.html

12.1 42. linear regression.pdf

12. Linear regression analysis.mp4

14:57

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.mp4

08:18

2. Feature generation.html

3.1 44. datetime data.pdf

3. Extracting day, month and year.mp4

07:34

4. Date element extraction.html

5.1 45. feature encoding.pdf

5. Encoding features - LabelEncoder.mp4

06:15

6. Feature encoding.html

7.1 46. feature binning.pdf

7. Categorizing numeric feature.mp4

10:04

8. Feature binning.html

9.1 46. feature mapping.pdf

9. Manual feature encoding.mp4

06:26

10. Feature mapping.html

11.1 47. creating dummies.pdf

11. Converting features into dummy.mp4

05:54

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.mp4

09:02

2. Feature selection.html

3.1 49. standard scaling.pdf

3. Scaling features - StandardScaler.mp4

05:30

4. Standard scaling.html

5.1 50. minmax scaler.pdf

5. Scaling features - MinMaxScaler.mp4

04:45

6. MinMax scaling.html

7.1 51. PCA.pdf

7. Dimensionality reduction with PCA.mp4

12:58

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.mp4

09:29

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.mp4

10:32

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.mp4

07:16

6. Decision tree regressor.html

7.1 3. RFR model ML.pdf

7. Random forest regressor ML model.mp4

07:58

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.mp4

13:46

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.mp4

08:15

5. Decision tree classification.html

6.1 6. RFC model ML.pdf

6. Random forest classification ML model.mp4

05:55

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.mp4

11:50

2. Calculating WCSS.html

3.1 7. selecting best k.pdf

3. Selecting optimal number of clusters.mp4

03:32

4. Plotting Elbow chart.html

5.1 8. developing k means.pdf

5. Application of KMeans machine learning.mp4

07:51

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

Description

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

What You'll Learn?

You will master the fundamentals of data analytics, including facts and theories, statistical analysis, hypothesis testing, and machine learning.
You will learn how to apply conditional formatting in Excel to visually highlight key trends, insights, and anomalies within your data.
You will learn essential Excel formulas and functions such as SUM, AVERAGE, COUNT, IF statements and MORE, enabling you to manipulate data effectively.
You will learn to utilize Excel's lookup functions (VLOOKUP, HLOOKUP, XLOOKUP) to efficiently search for and retrieve specific information within datasets.
You will learn various graph and chart types in Excel for data visualization, including bar charts, pie charts, scatter plots, and more to communicate insights.
You will learn advanced analysis using PivotTables and PivotCharts, enabling you to analyze, and visualize complex datasets with ease and interactivity.
You will learn to use Excel's built-in data analysis tools for statistical analysis, i.e., descriptive statistics, t-tests, ANOVA, correlation, and regression.
You will learn to design and create dynamic DASHBOARD in Excel, by a visually interactive format for effective decision-making and reporting.
You will learn the important Python programming basics such as variables naming, data types, lists, dictionaries, dataframes, sets, loops, functions etc.
You will master a range of methods and techniques for data cleaning, sorting, filtering, data manipulation, transformation, and data preprocessing in Python.
You will learn to use Python for data visualizations, exploratory data analysis, statistical analysis, hypothesis testing methods and machine learning models.
You will work on practical data analysis projects to apply learned skills. Enhance problem-solving abilities through hands-on data analysis exercises.

Who is this for?

Those who are highly interested in learning complete data analytics using Python.

Individuals aiming to develop comprehensive knowledge in data cleaning, analysis, visualization, and dashboard creation in Excel.

This course is NOT for those who are interested to learn data science or advanced machine learning application.

What You Need to Know?

Access to computer and internet

Basic computer literacy

No coding experience required

Dedication, patience and perseverance

More details

Description
Are you ready to embark on a journey into the world of data analytics? Welcome to Data Analytics 360, where you'll master two of the most powerful tools in the field: Python and Excel. In this comprehensive course, you'll dive deep into the foundations of data analysis, from basic statistical concepts to advanced machine learning techniques.

Master the Fundamentals: Gain a solid understanding of data analytics principles, including statistical analysis, hypothesis testing, and machine learning. Whether you're new to the field or looking to sharpen your skills, this course provides the perfect starting point.

Excel for Data Analysis: Unlock the full potential of Excel as a data analysis tool. Learn essential formulas and functions, harness the power of conditional formatting to identify trends and anomalies, and utilize lookup functions for efficient data retrieval. Discover the art of data visualization with various chart types and master advanced analysis with PivotTables and PivotCharts.

Python Essentials: Dive into Python programming basics, from variables and data types to loops and functions. Explore methods for data cleaning, sorting, filtering, and manipulation, as well as techniques for exploratory data analysis and hypothesis testing. Harness the power of Python libraries for data visualization and machine learning.

Hands-on Projects: Put your skills to the test with practical data analysis projects. From cleaning and preprocessing data to building machine learning models, you'll tackle real-world challenges and enhance your problem-solving abilities along the way.

Become a Data Analyst: By the end of this course, you'll have the knowledge and skills to excel as a data analyst. Whether you're looking to advance your career or explore new opportunities, Data Analytics 360 equips you with the tools you need to succeed in the world of data.

Enroll now and take the first step towards becoming a proficient data analyst with Data Analytics 360.
Who this course is for:
Those who are highly interested in learning complete data analytics using Python.
Individuals aiming to develop comprehensive knowledge in data cleaning, analysis, visualization, and dashboard creation in Excel.
This course is NOT for those who are interested to learn data science or advanced machine learning application.

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At StatElite Academy, our mission is to democratize access to statistical knowledge and empower individuals to excel in the realm of data analysis and interpretation. We believe that statistics is not merely a tool for academics or professionals in specific fields but a vital skill set for anyone navigating today's data-driven world. Through comprehensive courses and hands-on training, we aim to break down complex statistical concepts into easily understandable modules, fostering a community of lifelong learners equipped with the analytical prowess to tackle real-world problems.Our vision is to cultivate a global network of statistically literate individuals who not only possess the technical proficiency to analyze data but also the creative insight to derive meaningful insights. By emphasizing practical applications and real-world scenarios, we strive to bridge the gap between theory and practice, enabling our students to thrive in diverse industries and embark on successful freelance careers. At StatElite Academy, we envision a future where statistical literacy is not just a skill but a cornerstone of informed decision-making and innovation.

Udemy

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Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.