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

سرفصل های دوره

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

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

5 - 4.Application-DA.pdf

5 - Realworld application of data analysis

3 - Data Analysis Tools Techniques and Methods

6 - 9.Methods-of-DC.pdf

6 - Various aspects of data cleaning

7 - 20.Joining.pdf

7 - Various aspects of Joining datasets

8 - Methods of exploratory data analysis Part 1

9 - Methods of exploratory data analysis Part 2

10 - 12.13.14.Methods-of-EDA.pdf

10 - Methods of exploratory data analysis Part 3

4 - Statistical Analysis Methods and Techniques

11 - 6.Sampling-methods.pdf

11 - Population vs sample and its methods

12 - 23.Types-of-Statistics.pdf

12 - Types of statistical data analysis

13 - A Recap on descriptive statistics methods

14 - Inferential statistics Part 1 Ttests and ANOVA

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

15 - Inferential statistics Part 2 Relationships measures

16 - 28.Lin-reg.pdf

16 - Inferential statistics Part 3 Linear regression

5 - Clarifying the Concept of Hypothesis Testing

17 - 36.Hypothesis-Testing.pdf

17 - Hypothesis testing for inferential statistics

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

18 - Selecting statistical test and assumption testing

19 - 38.CSP.pdf

19 - Confidence level significance level pvalue

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

20 - Making decision and conclusion on findings

21 - 40.Example-HT.pdf

21 - AZ statistical analysis and hypothesis testing

6 - Data Transformation and Visualisation Methods

22 - Techniques for data transformation Part 1

23 - 17.18.Mthods-of-DTP.pdf

23 - Techniques for data transformation Part 2

24 - Several methods of data visualization Part 1

25 - Several methods of data visualization Part 2

26 - 45.46.47.Data-Visualization-Methods.pdf

26 - Several methods of data visualization Part 3

7 - Data Modeling with Machine Learning Model

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

27 - Importance of ML in data analytics

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

28 - Widely used machine learning models

29 - 43.steps-in-ML.pdf

29 - Steps in developing machine learning model

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

34 - Assigning variable names correctly

35 - Various data types and data structures

36 - Converting and casting data types

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

39 - Comparison operators

40 - Logical operators and or not

41 - Operators in Python Programming.html

41 - operators-in-python-programming.zip

11 - Dealing with Data Structures

42 - Lists creation indexing slicing modifying

43 - Sets unique elements operations

44 - Dictionaries keyvalue pairs methods

45 - Several data structures.html

45 - dealing-with-data-structures.zip

12 - Conditionals Looping and Functions

46 - Conditional statements if elif else

47 - Nested logical expressions in conditions

48 - Looping structures for loops while loops

49 - Defining creating and calling functions

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

52 - 2.identify-missing-values.pdf

52 - Identifying missing or null values

53 - 3.imputing-missing-values.pdf

53 - Method of missing value imputation

54 - 4.checking-data-types.pdf

54 - Exploring data types in a dataframe

55 - 5.removing-inconsistent-value.pdf

55 - Dealing with inconsistent values

56 - 6.assigning-data-type.pdf

56 - Assigning correct data types

57 - 7.dealing-with-duplicates.pdf

57 - Dealing with duplicated values

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

60 - 9.boolean-filtering.pdf

60 - Filtering data with boolean indexing

61 - 10.query.pdf

61 - Query method for precise filtering

62 - 11.is-in.pdf

62 - Filtering data with isin method

63 - 12.loc-and-iloc.pdf

63 - Slicing dataframe with loc and iloc

64 - 13.combining-conditions.pdf

64 - Filtering data for many conditions

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

67 - 15.concatenating-data.pdf

67 - Concatenate dataframes vertically

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

70 - 17.descriptive.pdf

70 - Descriptive statistics and analysis

71 - 18.group-by.pdf

71 - Group by data analysis method

72 - 19.pivot-table.pdf

72 - Pivot table analysis all in one

73 - 20.crosstab.pdf

73 - Crosstabulation analysis method

74 - 21.correl.pdf

74 - Correlation analysis for numeric data

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

77 - 23.bar-chart.pdf

77 - Getting started with bar charts

78 - 24.stacked-or-clustered.pdf

78 - Stacked and clustered bar charts

79 - 25.pie-chart.pdf

79 - Pie chart for percentage analysis

80 - 26.line-plot.pdf

80 - Line chart for grouping data analysis

81 - 27.histogram.pdf

81 - Exploring distribution with histogram

82 - 28.scatterplot.pdf

82 - Correlation analysis via scatterplot

83 - 29.heatmap.pdf

83 - Matrix visualisation with heatmap

84 - 30.boxplot.pdf

84 - Boxplot statistical visualisation method

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

87 - 32.normality-test.pdf

87 - Shapiro Wilk test of normality

88 - 33.square-root-transformation.pdf

88 - Starting with square root transformation

89 - 34.log-transformation.pdf

89 - Logarithmic transformation method

90 - 35.boxcox-transformation.pdf

90 - Boxcox power transformation method

91 - 36.yeojohnson-transformation.pdf

91 - YeoJohnson power transformation method

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

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

94 - Independent sample ttest

95 - 39.one-way-anova.pdf

95 - One way Analysis of Variance

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

96 - Chi square test for independence

97 - 41.pearson-correlation.pdf

97 - Pearson correlation analysis

98 - 42.linear-regression.pdf

98 - Linear regression analysis

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

101 - 44.datetime-data.pdf

101 - Extracting day month and year

102 - 45.feature-encoding.pdf

102 - Encoding features LabelEncoder

103 - 46.feature-binning.pdf

103 - Categorizing numeric feature

104 - 46.feature-mapping.pdf

104 - Manual feature encoding

105 - 47.creating-dummies.pdf

105 - Converting features into dummy

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

108 - 49.standard-scaling.pdf

108 - Scaling features StandardScaler

109 - 50.minmax-scaler.pdf

109 - Scaling features MinMaxScaler

110 - 51.PCA.pdf

110 - Dimensionality reduction with PCA

111 - 52.train-test-split.pdf

111 - Splitting into train and test set

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

114 - 2.DTR-model-ML.pdf

114 - Decision tree regressor machine learning

115 - 3.RFR-model-ML.pdf

115 - Random forest regressor machine learning

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

118 - 5.DTC-model-ML.pdf

118 - Decision tree classification machine learning

119 - 6.RFC-model-ML.pdf

119 - Random forest classification machine learning

120 - Classification machine learning.html

120 - classification-machine-learning.zip

24 - Data Segmentation with KMeans Clustering

121 - Calculating within cluster sum of squares

122 - 7.selecting-best-k.pdf

122 - Selecting optimal number of clusters

123 - 8.developing-k-means.pdf

123 - Application of KMeans machine learning

124 - Data segmentation with KMeans clustering.html

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

25 - Final Project Sports Data Analytics

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تولید کننده: Udemy