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.
Data Science Jumpstart with 10 Projects Course
3h 12m 21s
English
Paid
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.
About the Author: Talkpython
Talk Python to Me is a weekly podcast hosted by Michael Kennedy. The show covers a wide array of Python topics as well as many related topics (e.g. MongoDB, AngularJS, DevOps).
Watch Online 104 lessons
0:00
/ #1: Welcome
All Course Lessons (104)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Welcome Demo | 00:51 | |
| 2 | Installing Jupyter in a Virtual Environment | 02:01 | |
| 3 | Running in Github Codespaces | 01:37 | |
| 4 | How to use Jupyter | 02:09 | |
| 5 | How to use VS Code | 01:11 | |
| 6 | Remember the Exercises | 00:27 | |
| 7 | Intro csv v2 | 00:34 | |
| 8 | Loading CSV data from a ZIP file with Pandas and Pyarrow | 05:26 | |
| 9 | Summary stats in Pandas using describe, dtypes, and quantile | 06:35 | |
| 10 | Pearson and Spearman Correlations in Pandas and Heatmaps | 05:36 | |
| 11 | Understanding Pandas Categoricals with value_counts and Cross Tabulations | 04:50 | |
| 12 | Visualizations in Pandas, with Histograms, Scatterplots, and Barplots | 08:37 | |
| 13 | Summary | 00:25 | |
| 14 | Intro excel | 00:42 | |
| 15 | Create an Excel in Pandas with to_excel | 01:46 | |
| 16 | Read Excel file in Pandas with read_excel and Pyarrow | 01:31 | |
| 17 | Understanding Counts and Frequencies of Missing Data in Pandas with isna, any, sum, and mean | 03:03 | |
| 18 | Quantifying Strings with filter and value_counts | 02:07 | |
| 19 | Understanding Numbers with Correlations, Scatterplots, and Histograms | 03:33 | |
| 20 | Writing and Formatting Excel Sheets in Pandas with to_excel and XlsxWriter add_format | 01:49 | |
| 21 | Summary | 00:11 | |
| 22 | Intro | 00:15 | |
| 23 | Loading Data for Merging with Pyarrow | 00:57 | |
| 24 | Merging Dataframes with the merge method and left_on, right_on parameters | 01:34 | |
| 25 | Validating one to one and one to many merges | 02:51 | |
| 26 | Debugging Merging by piping dataframe size | 02:36 | |
| 27 | Cleanup columns after merging with loc | 02:19 | |
| 28 | Export Merged data to Excel | 00:56 | |
| 29 | Merging summary | 00:31 | |
| 30 | Intro grouping | 00:38 | |
| 31 | Loading Retail Data from Excel into Pandas Dataframe | 00:33 | |
| 32 | Using Feather and Pyarrow to Speed up loading Retail Data in Pandas | 00:49 | |
| 33 | Exploratory Data Analysis (EDA) in Pandas with describe, histograms, and value_counts | 03:48 | |
| 34 | Aggregating in Pandas to Calculate Sales by Year | 02:44 | |
| 35 | Using Groupby in Pandas to visualize Sales by country | 06:06 | |
| 36 | Using Grouper in Pandas to Groupby by Month Frequency | 03:36 | |
| 37 | Grouping by Month and Country and Visualizing with a Line Plot | 05:31 | |
| 38 | Summary | 00:26 | |
| 39 | Intro cleaning | 00:37 | |
| 40 | Loading Multiple Files into a Single Pandas Datafarme with Glob | 00:47 | |
| 41 | Understanding the Heart Data to Cleanup | 02:47 | |
| 42 | Fixing the Age Column Type to Int8 | 00:44 | |
| 43 | Converting the Numeric Sex Column into a String | 01:18 | |
| 44 | Converting the Chest Pain Column into an Int8 | 00:49 | |
| 45 | Dealing with ? Characters in the Trestbps Numeric Column | 02:25 | |
| 46 | Creating a Function to Repeat Common Cleanup in the Chol Column | 03:08 | |
| 47 | Using the Cleanup Function for the Fbs Column | 01:05 | |
| 48 | Fixing the Restecg Column | 01:28 | |
| 49 | Fixing the Thalach Column | 00:14 | |
| 50 | Fixing the Exang Column | 00:15 | |
| 51 | Updating the Cleanup Function to Clean the Oldpeak Column | 00:23 | |
| 52 | Cleaning the Slope Column | 00:19 | |
| 53 | Cleaning the Ca Column | 00:18 | |
| 54 | Converting Numeric Values to Catgoricals with the Thal Column | 00:39 | |
| 55 | Fixing the Num Column | 01:07 | |
| 56 | Comparing Memory usage in Pandas with memory_usage | 00:50 | |
| 57 | Refactoring to a Function in Pandas for Cleanup | 04:19 | |
| 58 | Cleaning summary | 00:06 | |
| 59 | Intro time series air quality dataset | 00:31 | |
| 60 | Load CSV file from a Zip file with Pandas | 00:51 | |
| 61 | Checking for Missing Values and Shape in Pandas | 00:52 | |
| 62 | Parsing Dates Using Format Strings and to_datetime | 02:04 | |
| 63 | Rename columns in Pandas to Remove Invalid Characters | 02:36 | |
| 64 | Make a Function to Clean up Pandas Data | 00:52 | |
| 65 | Converting Dates to UTC in Pandas | 00:57 | |
| 66 | Converting Dates to Italian time in Pandas and pytz | 01:30 | |
| 67 | Making Line Plots for Time Series Data in Pandas | 03:24 | |
| 68 | Interpolating and Filling in Missing values in Pandas | 03:27 | |
| 69 | Resampling Time Series Data in Pandas with resample | 02:30 | |
| 70 | Creating 7 Day Rolling Averages in Pandas with rolling | 01:45 | |
| 71 | Updating the Function with Cleanup Functionality | 00:16 | |
| 72 | Summary | 00:22 | |
| 73 | Intro text v2 | 00:25 | |
| 74 | Load movie review text data from a directory | 01:32 | |
| 75 | Exploring the str attribute in Pandas for String manipulation | 00:55 | |
| 76 | Using Spacy to Remove Stop words in Pandas | 02:44 | |
| 77 | Using scikit-learn to calculate Tfidf for Pandas text | 01:44 | |
| 78 | Using XGBoost to Create a Classification Model | 02:40 | |
| 79 | Predicting Values with XGBoost and Pandas | 01:40 | |
| 80 | Intro v2 | 00:21 | |
| 81 | Combining Multiple Datasets with Pandas and concat | 02:00 | |
| 82 | Exploring heart disease with aggregations and scatterplots | 05:01 | |
| 83 | Preparing a Pandas Dataset to Create an XGBoost Model | 04:59 | |
| 84 | Tuning an XGBoost Model with Hyperopt | 06:02 | |
| 85 | Using a Confusion matrix to Understand the Model | 01:48 | |
| 86 | Ml summary | 00:09 | |
| 87 | Intro SQL | 00:13 | |
| 88 | Load CSV data into a Pandas dataframe and cleaning it | 01:32 | |
| 89 | Using SqlAlchemy to Connect to a SQLite Database | 00:55 | |
| 90 | Create a database table with Pandas using to_sql | 00:31 | |
| 91 | Query a SQLite table from Pandas using read_sql | 01:19 | |
| 92 | Query a SQLite table with Pandas | 01:57 | |
| 93 | Visualize SQLite Data using Pandas | 01:54 | |
| 94 | Summary SQL | 00:27 | |
| 95 | Intro plotly | 00:11 | |
| 96 | Load CSV data into Pandas dataframe | 00:22 | |
| 97 | Clean Pandas data with a function for plotly | 01:45 | |
| 98 | Creating a Line Plot in Plotly for Pandas | 02:01 | |
| 99 | Creating a Bar plot in Plotly | 02:29 | |
| 100 | Creating a Scatter plot in Plotly | 03:41 | |
| 101 | Creating a Dashboard with Dash and Plotly Graphs | 01:43 | |
| 102 | Creating a Plotly Dashboard using Dash with Widgets | 01:10 | |
| 103 | Summary plotly | 00:08 | |
| 104 | Conclusion | 01:17 |
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