English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 104 Lessons (3h 12m) | 1000 MB
This course will empower you with the skills and tools to dive deep into data science using Python. We assume you have a foundational understanding of Python but not data science concepts. This course exposes you to the same tools that data scientists, data engineers, analysts use data to tackle real-world challenges.
In this course, you will:
- Delve into loading, cleaning, summarizing, and basic statistics with both CSV and Excel data.
- Master the art of combining and reshaping datasets to uncover hidden patterns in the Retail Data Insights project.
- Understand missing data handling, abnormal data recognition, and foundational machine learning techniques through Health Data Deep Dives.
- Create models to explore Air Quality Trends & Movie Reviews.
- Construct interactive dashboards using Plotly and explore SQL databases in the Interactive
- Dashboards & SQL Exploration section.
- Harness powerful libraries such as Pandas, Matplotlib, Plotly, and more.
Table of Contents
1 Welcome
2 Installing Jupyter in a Virtual Environment
3 Running in Github Codespaces
4 How to use Jupyter
5 How to use VS Code
6 Remember the Exercises
7 Intro csv v2
8 Loading CSV data from a ZIP file with Pandas and Pyarrow
9 Summary stats in Pandas using describe, dtypes, and quantile
10 Pearson and Spearman Correlations in Pandas and Heatmaps
11 Understanding Pandas Categoricals with value_counts and Cross Tabulations
12 Visualizations in Pandas, with Histograms, Scatterplots, and Barplots
13 Summary
14 Intro excel
15 Create an Excel in Pandas with to_excel
16 Read Excel file in Pandas with read_excel and Pyarrow
17 Understanding Counts and Frequencies of Missing Data in Pandas with isna, any, sum, and mean
18 Quantifying Strings with filter and value_counts
19 Understanding Numbers with Correlations, Scatterplots, and Histograms
20 Writing and Formatting Excel Sheets in Pandas with to_excel and XlsxWriter add_format
21 Summary_2
22 Intro
23 Loading Data for Merging with Pyarrow
24 Merging Dataframes with the merge method and left_on, right_on parameters
25 Validating one to one and one to many merges
26 Debugging Merging by piping dataframe size
27 Cleanup columns after merging with loc
28 Export Merged data to Excel
29 Merging summary
30 Intro grouping
31 Loading Retail Data from Excel into Pandas Dataframe
32 Using Feather and Pyarrow to Speed up loading Retail Data in Pandas
33 Exploratory Data Analysis (EDA) in Pandas with describe, histograms, and value_counts
34 Aggregating in Pandas to Calculate Sales by Year
35 Using Groupby in Pandas to visualize Sales by country
36 Using Grouper in Pandas to Groupby by Month Frequency
37 Grouping by Month and Country and Visualizing with a Line Plot
38 Summary_3
39 Intro cleaning
40 Loading Multiple Files into a Single Pandas Datafarme with Glob
41 Understanding the Heart Data to Cleanup
42 Fixing the Age Column Type to Int8
43 Converting the Numeric Sex Column into a String
44 Converting the Chest Pain Column into an Int8
45 Dealing with . Characters in the Trestbps Numeric Column
46 Creating a Function to Repeat Common Cleanup in the Chol Column
47 Using the Cleanup Function for the Fbs Column
48 Fixing the Restecg Column
49 Fixing the Thalach Column
50 Fixing the Exang Column
51 Updating the Cleanup Function to Clean the Oldpeak Column
52 Cleaning the Slope Column
53 Cleaning the Ca Column
54 Converting Numeric Values to Catgoricals with the Thal Column
55 Fixing the Num Column
56 Comparing Memory usage in Pandas with memory_usage
57 Refactoring to a Function in Pandas for Cleanup
58 Cleaning summary
59 Intro time series air quality dataset
60 Load CSV file from a Zip file with Pandas
61 Checking for Missing Values and Shape in Pandas
62 Parsing Dates Using Format Strings and to_datetime
63 Rename columns in Pandas to Remove Invalid Characters
64 Make a Function to Clean up Pandas Data
65 Converting Dates to UTC in Pandas
66 Converting Dates to Italian time in Pandas and pytz
67 Making Line Plots for Time Series Data in Pandas
68 Interpolating and Filling in Missing values in Pandas
69 Resampling Time Series Data in Pandas with resample
70 Creating 7 Day Rolling Averages in Pandas with rolling
71 Updating the Function with Cleanup Functionality
72 Summary_4
73 Intro text v2
74 Load movie review text data from a directory
75 Exploring the str attribute in Pandas for String manipulation
76 Using Spacy to Remove Stop words in Pandas
77 Using scikit-learn to calculate Tfidf for Pandas text
78 Using XGBoost to Create a Classification Model
79 Predicting Values with XGBoost and Pandas
80 Intro v2
81 Combining Multiple Datasets with Pandas and concat
82 Exploring heart disease with aggregations and scatterplots
83 Preparing a Pandas Dataset to Create an XGBoost Model
84 Tuning an XGBoost Model with Hyperopt
85 Using a Confusion matrix to Understand the Model
86 Ml summary
87 Intro SQL
88 Load CSV data into a Pandas dataframe and cleaning it
89 Using SqlAlchemy to Connect to a SQLite Database
90 Create a database table with Pandas using to_sql
91 Query a SQLite table from Pandas using read_sql
92 Query a SQLite table with Pandas
93 Visualize SQLite Data using Pandas
94 Summary SQL
95 Intro plotly
96 Load CSV data into Pandas dataframe
97 Clean Pandas data with a function for plotly
98 Creating a Line Plot in Plotly for Pandas
99 Creating a Bar plot in Plotly
100 Creating a Scatter plot in Plotly
101 Creating a Dashboard with Dash and Plotly Graphs
102 Creating a Plotly Dashboard using Dash with Widgets
103 Summary plotly
104 Conclusion
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