Learn Data Science with Python, Pandas, Scikit-learn, and more! | 4 Projects | 100+ exercises
Welcome to the Python for Data Science Bootcamp: From Zero to Hero
In this practical course, we'll learn how to collect data, clean data, make visualizations and build a machine learning model using Python.
The main goal of this course is to take your programming and analytical skills to the next level to build your career in Data Science. To achieve this goal, we're going to solve hundreds of exercises and many cool projects that will help you put into practice all the programming concepts used in Data Science.
We'll learn the top Python Libraries used in Data Science such as Pandas, Numpy and Scikit Learn and we will use them to learn to solve tasks data scientists deal with on a daily basis (Data Cleaning, Data Visualization, Data Collection and Model Building)
This course covers 4 main sections:
1. Python for Data Science Crash Course: In the first section, we'll learn all the Python core concepts you need to know for Data Science. We'll learn how to use variables, lists, dictionaries and more.
2. Python for Data Analysis: We'll learn Python libraries used for data analysis such as Pandas and Numpy. Both are great tools for exploring and working with data. We'll use Pandas and Numpy to deal with data science tasks such as cleaning and preparing data.
3. Python for Data Visualization: In the third section, we'll learn how to make static and interactive visualizations with Pandas. Also, I'll show you some techniques to properly make data visualization.
4. Machine Learning with Python: In the fourth section, we'll learn scikit-learn by solving a text classification problem in Python. This is the most popular machine learning library in Python and we'll not only learn how to implement machine learning algorithms in Python but also we'll learn the core concepts behind the most common algorithms using practical examples.
Who this course is for?
- Beginners who want to learn Data Science with Python from scratch
- Excel users who want to take their skills to the next level
Course Curriculum
- Section Overview + All The Files for This Section
- Introduction to Pandas (6:24)
- How to Create a Dataframe (16:23)
- Different Ways to Display a Dataframe (6:48)
- Basic Attributes, Methods and Functions (11:50)
- Selecting One Column from a Dataframe (5:53)
- Selecting Two or More Columns from a Dataframe (5:35)
- Add New Column to a Dataframe (Simple Assignment) (9:59)
- Add New Column to a Dataframe with assign() and insert() (6:41)
- Operations on Dataframes (columns and rows) (8:10)
- The value_counts() method (4:10)
- Important Note
- Sort a Dataframe with sort_values() (9:37)
- The set_index() and sort_index() methods (6:56)
- Rename Columns and Index with rename() (5:58)
- Exercise for this Section
- Download All The Files Used In this Section
- Filter a Dataframe Based on 1 Condition (14:14)
- Creating a Conditional Column from 2 Choices np.where() (10:29)
- Filter a Dataframe Based on 2 or More Conditions (12:07)
- Creating a Conditional Column from More Than 2 Choices np.select() (12:51)
- The .isin() Method (7:17)
- Find Duplicate Rows with the .duplicated() Method (17:55)
- Drop Duplicate Elements with the .drop_duplicates() Method (10:17)
- Get and Count Unique Values The unique() and nunique() methods (3:54)
- Download All The Files Used In this Section
- Differences between the loc() and iloc() method (7:15)
- First Look at The Dataset Setting Index and Selecting Columns (5:31)
- Selecting elements by index label with .loc() (20:20)
- Selecting elements by index position with .iloc() (13:02)
- Set New Value for a Cell in a Dataframe (9:47)
- Drop Rows Or Columns from a Dataframe (9:36)
- Create Random Sample with the sample() Method (7:44)
- Filter a DataFrame with the query() method (12:29)
- The apply() Method (6:09)
- Lambda function + apply() method (17:52)
- Make a Copy of a Dataframe with copy() (Deep Copy vs Shallow Copy) (7:18)
- Dataset Overview (3:46)
- Identify Missing Data with the isnull() Method (10:28)
- Dealing with Missing Data: Remove a column or row with .drop, .dropna or .isnull (12:35)
- Dealing with Missing Data: Replace NaN by the mean, median, mode with .fillna() (14:06)
- Extracting data with split() and extract() methods (15:44)
- How to Identify and Deal with Outliers (17:26)
- Dealing with inconsistent capitalization lower(), upper(), title() (4:52)
- Remove blank spaces with strip(), lstrip(), and rstrip() (4:41)
- Replace strings with replace() or sub() (8:14)
- The Dataset, Selecting a Sample Datafame and The Problem to Solve (8:35)
- Dealing with Imbalanced Classes: Undersampling and Oversampling (17:19)
- Splitting data into train and test set (6:54)
- Text Representation with Bag of Words: CountVectorizer and TF-IDF (17:59)
- Turning our text data into numerical vectors (7:00)
- Model Selection: Types of Machine Learning Algorithms for out Project (2:05)
- Support Vector Machines (SVM) (6:23)
- Decision Tree (4:34)
- Naive Bayes (5:11)
- Logistic Regression (2:22)
- Model Evaluation - Confusion Matrix (6:32)
- Mean Accuracy (3:31)
- F1 Score (4:55)
- Classification Report (1:44)
- Tuning the Model - GridSearchCV (3:38)
Your Instructor
Hi! My name is Frank Andrade. Probably you've already watched my YouTube tutorials or read my Python guides on Medium. If not, click on my photo, to check them out.
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