Data Science Jumpstart with 10 Projects Course
3h 12m 21s
English
Paid
Course description
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.
Read more about the course
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.
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| # | Title | Duration |
|---|---|---|
| 1 | Welcome | 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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