Data Science Jumpstart with 10 Projects Course

03:12:21 Inglés Premium 25/04/2024 104 videos

Descripción del curso

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.

Curriculum

Section 1: Module 1

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

About the Instructor

Talkpython

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