Python Data Analyst: 25 Days for A-Z Data Analysis in Python

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StatElite Academy,Shahriar's Sight Academy

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1 - Start Here MUST Follow the Instructions

1 - Instructions to accomplish the course.html

2 - Python cheatsheet for data analysis.html

2 - python-data-analysis-sheet.zip

3 - Resources.zip

3 - Resources used in the course.html

2 - Data Analysis and Its Application

4 - 1.Un-DA.pdf

4 - Understanding analyzing data.mp4

07:11

5 - 4.Application-DA.pdf

5 - Realworld application of data analysis.mp4

04:25

3 - Data Analysis Tools Techniques and Methods

6 - 9.Methods-of-DC.pdf

6 - Various aspects of data cleaning.mp4

14:12

7 - 20.Joining.pdf

7 - Various aspects of Joining datasets.mp4

08:50

8 - Methods of exploratory data analysis Part 1.mp4

12:15

9 - Methods of exploratory data analysis Part 2.mp4

11:17

10 - 12.13.14.Methods-of-EDA.pdf

10 - Methods of exploratory data analysis Part 3.mp4

15:36

4 - Statistical Analysis Methods and Techniques

11 - 6.Sampling-methods.pdf

11 - Population vs sample and its methods.mp4

11:05

12 - 23.Types-of-Statistics.pdf

12 - Types of statistical data analysis.mp4

04:58

13 - A Recap on descriptive statistics methods.mp4

03:14

14 - Inferential statistics Part 1 Ttests and ANOVA.mp4

06:54

15 - 25.26.27.methods-of-ds-and-is.pdf

15 - Inferential statistics Part 2 Relationships measures.mp4

03:16

16 - 28.Lin-reg.pdf

16 - Inferential statistics Part 3 Linear regression.mp4

11:53

5 - Clarifying the Concept of Hypothesis Testing

17 - 36.Hypothesis-Testing.pdf

17 - Hypothesis testing for inferential statistics.mp4

06:03

18 - 37.Selecting-Appropriate-Statistical-Test.pdf

18 - Selecting statistical test and assumption testing.mp4

10:26

19 - 38.CSP.pdf

19 - Confidence level significance level pvalue.mp4

03:37

20 - 39.Making-Decision-and-Conclusion.pdf

20 - Making decision and conclusion on findings.mp4

02:55

21 - 40.Example-HT.pdf

21 - AZ statistical analysis and hypothesis testing.mp4

07:02

6 - Data Transformation and Visualisation Methods

22 - Techniques for data transformation Part 1.mp4

06:11

23 - 17.18.Mthods-of-DTP.pdf

23 - Techniques for data transformation Part 2.mp4

04:07

24 - Several methods of data visualization Part 1.mp4

04:04

25 - Several methods of data visualization Part 2.mp4

05:38

26 - 45.46.47.Data-Visualization-Methods.pdf

26 - Several methods of data visualization Part 3.mp4

07:17

7 - Data Modeling with Machine Learning Model

27 - 41.ML-in-Data-Analysis.pdf

27 - Importance of ML in data analytics.mp4

06:07

28 - 42.Types-of-Machine-Learning.pdf

28 - Widely used machine learning models.mp4

11:11

29 - 43.steps-in-ML.pdf

29 - Steps in developing machine learning model.mp4

07:43

8 - Setting Up Python and Jupyter Notebook

30 - Installing Python and Jupyter Notebook Mac.html

30 - Mac.pdf

31 - Installing Python and Jupyter Notebook Windows.html

31 - Windows.pdf

32 - More alternative methods Check the article.html

9 - Starting with Variables to Data Types

33 - Getting started with first python code.mp4

04:36

34 - Assigning variable names correctly.mp4

09:05

35 - Various data types and data structures.mp4

07:22

36 - Converting and casting data types.mp4

09:49

37 - Starting with Variables to Data Types.html

37 - starting-with-variables-to-data-types.zip

10 - Various Operators in Python Programming

38 - Arithmetic operators.mp4

06:44

39 - Comparison operators.mp4

07:36

40 - Logical operators and or not.mp4

07:06

41 - Operators in Python Programming.html

41 - operators-in-python-programming.zip

11 - Dealing with Data Structures

42 - Lists creation indexing slicing modifying.mp4

15:59

43 - Sets unique elements operations.mp4

07:51

44 - Dictionaries keyvalue pairs methods.mp4

09:23

45 - Several data structures.html

45 - dealing-with-data-structures.zip

12 - Conditionals Looping and Functions

46 - Conditional statements if elif else.mp4

07:04

47 - Nested logical expressions in conditions.mp4

09:57

48 - Looping structures for loops while loops.mp4

09:12

49 - Defining creating and calling functions.mp4

05:11

50 - Conditions loops and functions.html

50 - conditionals-looping-and-functions.zip

13 - Sequential Cleaning and Modifying Data

51 - 1.loading-data.pdf

51 - Preparing notebook and loading data.mp4

13:14

52 - 2.identify-missing-values.pdf

52 - Identifying missing or null values.mp4

05:02

53 - 3.imputing-missing-values.pdf

53 - Method of missing value imputation.mp4

13:23

54 - 4.checking-data-types.pdf

54 - Exploring data types in a dataframe.mp4

05:37

55 - 5.removing-inconsistent-value.pdf

55 - Dealing with inconsistent values.mp4

08:33

56 - 6.assigning-data-type.pdf

56 - Assigning correct data types.mp4

04:33

57 - 7.dealing-with-duplicates.pdf

57 - Dealing with duplicated values.mp4

05:20

58 - Sequential data cleaning and modifying.html

58 - data-loading-and-cleaning.zip

14 - Various Aspects of Data Manipulation

59 - 8.sorting-data.pdf

59 - Sorting data by column and order.mp4

05:51

60 - 9.boolean-filtering.pdf

60 - Filtering data with boolean indexing.mp4

08:48

61 - 10.query.pdf

61 - Query method for precise filtering.mp4

05:59

62 - 11.is-in.pdf

62 - Filtering data with isin method.mp4

05:37

63 - 12.loc-and-iloc.pdf

63 - Slicing dataframe with loc and iloc.mp4

10:21

64 - 13.combining-conditions.pdf

64 - Filtering data for many conditions.mp4

07:19

65 - Various aspects of data manipulation.html

65 - data-sorting-and-filtering.zip

15 - Merging and Concatenating Dataframes

66 - 14.joining-data.pdf

66 - Joining dataframes horizontally.mp4

07:26

67 - 15.concatenating-data.pdf

67 - Concatenate dataframes vertically.mp4

08:33

68 - Merging and concatenating dataframes.html

68 - merging-and-joining-dataframes.zip

16 - Applied Exploratory Data Analysis Methods

69 - 16.value-counts.pdf

69 - Frequency and percentage analysis.mp4

06:56

70 - 17.descriptive.pdf

70 - Descriptive statistics and analysis.mp4

09:04

71 - 18.group-by.pdf

71 - Group by data analysis method.mp4

05:41

72 - 19.pivot-table.pdf

72 - Pivot table analysis all in one.mp4

13:42

73 - 20.crosstab.pdf

73 - Crosstabulation analysis method.mp4

04:43

74 - 21.correl.pdf

74 - Correlation analysis for numeric data.mp4

08:37

75 - Applied exploratory data analysis.html

75 - applied-exploratory-data-analysis.zip

17 - Exploring Data Visualisations Methods

76 - 22.methods-used-in-visualisation.pdf

76 - Understanding visualisation tools.mp4

06:46

77 - 23.bar-chart.pdf

77 - Getting started with bar charts.mp4

12:18

78 - 24.stacked-or-clustered.pdf

78 - Stacked and clustered bar charts.mp4

09:40

79 - 25.pie-chart.pdf

79 - Pie chart for percentage analysis.mp4

06:23

80 - 26.line-plot.pdf

80 - Line chart for grouping data analysis.mp4

07:53

81 - 27.histogram.pdf

81 - Exploring distribution with histogram.mp4

10:30

82 - 28.scatterplot.pdf

82 - Correlation analysis via scatterplot.mp4

09:03

83 - 29.heatmap.pdf

83 - Matrix visualisation with heatmap.mp4

08:02

84 - 30.boxplot.pdf

84 - Boxplot statistical visualisation method.mp4

08:40

85 - Exploring data visualisations methods.html

85 - data-visualisations.zip

18 - Several Data Transformation Methods

86 - 31.check-distribution.pdf

86 - Investigating distribution of numeric data.mp4

21:31

87 - 32.normality-test.pdf

87 - Shapiro Wilk test of normality.mp4

10:57

88 - 33.square-root-transformation.pdf

88 - Starting with square root transformation.mp4

14:05

89 - 34.log-transformation.pdf

89 - Logarithmic transformation method.mp4

10:18

90 - 35.boxcox-transformation.pdf

90 - Boxcox power transformation method.mp4

08:51

91 - 36.yeojohnson-transformation.pdf

91 - YeoJohnson power transformation method.mp4

09:34

92 - Practical data transformation methods.html

92 - transformation-methods.zip

19 - Statistical Tests and Hypothesis Testing

93 - 37.one-sample-ttest.pdf

93 - One sample ttest.mp4

09:56

94 - 38.independent-sample-t-test.pdf

94 - Independent sample ttest.mp4

12:02

95 - 39.one-way-anova.pdf

95 - One way Analysis of Variance.mp4

14:55

96 - 40.chi-test-for-ind.pdf

96 - Chi square test for independence.mp4

07:18

97 - 41.pearson-correlation.pdf

97 - Pearson correlation analysis.mp4

10:28

98 - 42.linear-regression.pdf

98 - Linear regression analysis.mp4

14:57

99 - Statistical tests and hypothesis testing.html

99 - statistical-tests-and-hypothesis-testing.zip

20 - Exploring Feature Engineering Methods

100 - 43.feature-generation.pdf

100 - Generating new features.mp4

08:18

101 - 44.datetime-data.pdf

101 - Extracting day month and year.mp4

07:34

102 - 45.feature-encoding.pdf

102 - Encoding features LabelEncoder.mp4

06:15

103 - 46.feature-binning.pdf

103 - Categorizing numeric feature.mp4

10:05

104 - 46.feature-mapping.pdf

104 - Manual feature encoding.mp4

06:27

105 - 47.creating-dummies.pdf

105 - Converting features into dummy.mp4

05:54

106 - Feature engineering methods.html

106 - feature-engineering-methods.zip

21 - Data Preprocessing for Machine Learning

107 - 48.selecting-features.pdf

107 - Selecting features and target.mp4

09:02

108 - 49.standard-scaling.pdf

108 - Scaling features StandardScaler.mp4

05:30

109 - 50.minmax-scaler.pdf

109 - Scaling features MinMaxScaler.mp4

04:46

110 - 51.PCA.pdf

110 - Dimensionality reduction with PCA.mp4

12:58

111 - 52.train-test-split.pdf

111 - Splitting into train and test set.mp4

09:29

112 - Preprocessing for machine learning.html

112 - preprocessing-for-machine-learning.zip

22 - Predictive Analytics Regression Machine Learning

113 - 1.LR-model-ML.pdf

113 - Linear regression machine learning.mp4

10:32

114 - 2.DTR-model-ML.pdf

114 - Decision tree regressor machine learning.mp4

07:16

115 - 3.RFR-model-ML.pdf

115 - Random forest regressor machine learning.mp4

07:58

116 - Regression machine learning.html

116 - regression-machine-learning.zip

23 - Predictive Analytics Classification Machine Learning

117 - 4.LGR-model-ML.pdf

117 - Logistic regression machine learning.mp4

13:46

118 - 5.DTC-model-ML.pdf

118 - Decision tree classification machine learning.mp4

08:15

119 - 6.RFC-model-ML.pdf

119 - Random forest classification machine learning.mp4

05:55

120 - Classification machine learning.html

120 - classification-machine-learning.zip

24 - Data Segmentation with KMeans Clustering

121 - Calculating within cluster sum of squares.mp4

11:50

122 - 7.selecting-best-k.pdf

122 - Selecting optimal number of clusters.mp4

03:32

123 - 8.developing-k-means.pdf

123 - Application of KMeans machine learning.mp4

07:51

124 - Data segmentation with KMeans clustering.html

124 - data-segmentation-with-kmeans-clustering.zip

25 - Final Project Sports Data Analytics

Description

Master Python for A-Z Data Analysis and Become Pro Data Analyst with Basics to Hands-on Coding Exercises and Assignments

What You'll Learn?

You will get proficient in Python for thorough data analysis. Prepare for a career as a data analyst by acquiring practical skills and expertise.
You will master the fundamentals of data analytics, including facts and theories, statistical analysis, hypothesis testing, and machine learning.
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.
You will pass practical assignments, 85+ coding exercises, 10 quizzes with 100+ questions, on all the topics over the entire course.
You will accomplish one capstone project on Sport data analysis at the end to get the full view of data analysis workflow in Python.

Who is this for?

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

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

More details

Description
Welcome to the Data Analysis Bootcamp: A-Z Data Analysis in Python! In this comprehensive course, you'll embark on a journey from Python novice to proficient data analyst, equipped with the essential skills and knowledge to excel in the field.

Throughout this course, you will delve deep into the realm of Python programming, focusing on its application in data analysis. Starting from the basics, you'll master fundamental concepts such as variable naming, data types, lists, dictionaries, dataframes, sets, loops, and functions. With a solid foundation in Python, you'll seamlessly transition to advanced topics, including data cleaning, sorting, filtering, manipulation, transformation, and preprocessing.

But that's not all. As you progress, you'll learn how to harness the power of Python for data visualization, exploratory data analysis, statistical analysis, hypothesis testing, and even delve into the exciting world of machine learning. Through a combination of theoretical understanding and hands-on practice, you'll gain proficiency in a wide range of methods and techniques essential for data analysis.

What sets this course apart is its emphasis on practical application. You won't just learn the theory; you'll put your newfound knowledge to the test through practical data analysis projects and hands-on exercises. With over 85 coding exercises, 10 quizzes featuring 100+ questions, and practical assignments covering all topics, you'll have ample opportunities to reinforce your skills and enhance your problem-solving abilities.

As the culmination of your journey, you'll undertake a capstone project focused on sports data analysis. This final project will allow you to apply all the skills you've acquired throughout the course, providing you with a comprehensive understanding of the data analysis workflow in Python.

Whether you're a seasoned professional looking to upskill or someone just starting their journey in data analysis, this course is designed to equip you with the expertise and confidence needed to succeed. Join us on this exciting adventure and unlock your potential as a data analyst in Python.
Who this course is for:
Those who are highly interested in learning complete data analytics using Python.
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.

Shahriar's Sight Academy

Instructor's Courses

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, SQL, Tableau 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.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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