Data Science A-Z for Absolute Beginners
- Description
- Curriculum
- FAQ
- Reviews
Welcome to “Data Science A-Z for Absolute Beginners”. A Comprehensive Hands-On Course Covering the A-Z workflow of Data Science in Python. Start a life-changing trip into the ever-changing world of data science with our complete course for complete beginners. Participants in this hands-on program will learn about the A-Z data science workflow in great detail. They will gain both the theoretical information and practical skills they need to be successful in the field.
The first part of the course is an introduction to data science, explaining its central role, importance, and wide range of uses in different fields. Then, the participants will learn the ins and outs of data cleaning, including how to deal with outliers, missing numbers, and different types of data. Students will learn how to sort, filter, merge, and concatenate data using Python’s powerful pandas tool.
Beyond just cleaning up the data, exploratory data analysis (EDA) is now a natural part of the program. The participants will learn more about variables, group-by processes, frequencies, percentages, pivot tables, crosstabulation, and variable relationships. This will help them get better at turning raw data into useful information. The focus on real-world applications continues with a deep dive into data preprocessing, which includes building features, choosing them, and scaling them so that machine learning models are ready to use.
The course covers a wide range of algorithms, from basic ones like linear regression to more complex ones like xgboost and lightgbm, and helps students become experts in supervised regression and classification models. Clustering models like KMeans and DBSCAN make unsupervised learning the main topic. These models let people find hidden patterns in data without having to mark training samples.
One unique thing about this course is that it uses ChatGPT to make the experience more immersive through real-life problem-solving situations and interactive conversations. Not only will participants learn the basics of Python programming, they will also learn how to communicate clearly, turning complicated data science results into clear, useful insights for stakeholders.
Assessment is built right into the course structure, and a series of layered quizzes help students understand how to do everything in the data science process. By the end of the course, participants will have improved their Python programming skills, learned how to use important libraries like pandas, numpy, and scikit-learn, and gained a complete idea of how data science works.
This class is more than just a way to learn; it’s also a way to start a job. With a wide range of skills, participants will be ready to face challenges in the real world and confidently start a rewarding job in data science. Sign up now to get access to a world powered by data-driven insights.
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Stepping into the Python programming -
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Assigning variables and the rules of names
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Various data types in python programming
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Data type conversion and casting in python
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Applying arithmetic operations in python
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Utilizing comparison operators in python
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Using logical operators in python
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Python programming part 1
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Applying list for indexing, slicing and more
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Creating unique elements of sets and operations
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All about python dictionaries
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Performing Conditional statements (if, elif, else)
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Nesting logical expressions in conditional operations
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Looping structures (for loops, while loops)
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Defining, Creating and Calling functions
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Python programming part 2
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Getting started with a dataset
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Impute missing values with Simple-Imputer
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Rectify inconsistent variables and values
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Identify and assign correct data types
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Abolish duplicated data from the dataset
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QUIZ 1: Full Data Cleaning
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 5. If not, please ensure that you have successfully completed the instructions given in the lecture.
Then, you may proceed to take the QUIZ.
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Solution 1: Full Data Cleaning
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Sorting and arranging dataset
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Conditional filtering (and, or, not etc.)
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Merging dataset with extra features
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Concatenating data with extra data
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QUIZ 2: Full Data Manipulation
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 5. If not, please ensure that you have successfully completed the instructions given in the lecture. Additionally, you have to complete the QUIZ 1 successfully to complete this QUIZ.
Then, you may proceed to take the QUIZ.
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Solution 2: Full Data Manipulation
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Understanding exploratory data analysis
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Investigating Value Counts Analysis Technique
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Delving into Descriptive Statistics Analysis Technique
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Understanding Group By Analysis Method
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Mastering Pivot Table Analysis Method
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Unpacking Crosstabulation Analysis Method
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Exploring Correlation Analysis Method
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QUIZ 3: Full Exploratory Data Analysis
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 5. If not, please ensure that you have successfully completed the instructions given in the lecture. Additionally, you have to complete the QUIZ 1 and QUIZ 2 successfully to complete this QUIZ.
Then, you may proceed to take the QUIZ.
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Solution 3: Full Exploratory Data Analysis
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Testing normal distribution of numeric variables
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Square root data transformation method
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Logarithm data transformation method
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Box-cox data transformation method
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Yeo-Johnson data transformation method
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QUIZ 5: Various Data Transformation Methods
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 5. If not, please ensure that you have successfully completed the instructions given in the lecture. Additionally, you have to complete the QUIZ 1, QUIZ 2, QUIZ 3 and QUIZ 4 successfully to complete this QUIZ.
Then, you may proceed to take the QUIZ.
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Solution 5: Data Transformation Methods
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One way between groups ANOVA: Checking the difference
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Pearson correlation test: Checking the relationship
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Regression test: Checking the influence
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QUIZ 6: Hypothesis Testing
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 5. If not, please ensure that you have successfully completed the instructions given in the lecture. Additionally, you have to complete the QUIZ 1, QUIZ 2, QUIZ 3, QUIZ 4 and QUIZ 5 successfully to complete this QUIZ.
Then, you may proceed to take the QUIZ.
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Solution 6: Hypothesis Testing
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Feature engineering to generate significant variable
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Feature encoding to assign numeric values
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Techniques to create dummy variables
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Feature scaling for standardization and normalization
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Splitting data into training and testing set
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QUIZ 7: Full Data Preprocessing
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 5. If not, please ensure that you have successfully completed the instructions given in the lecture. Additionally, you have to complete the QUIZ 1, QUIZ 2, QUIZ 3, QUIZ 4, QUIZ 5, and QUIZ 6 successfully to complete this QUIZ.
Then, you may proceed to take the QUIZ.
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Solution 7: Full Data Preprocessing
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**Read It: IMPORTANT**
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Getting started: Linear regression ML model
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Decision Tree regressior ML model
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Random Forest regressor ML model
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Support Vector regressor ML model
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XGBoost regressor ML model
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QUIZ 8: Supervised ML model Part 1
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 5. If not, please ensure that you have successfully completed the instructions given in the lecture. Additionally, you have to complete the QUIZ 1, QUIZ 2, QUIZ 3, QUIZ 4, QUIZ 5, QUIZ 6 and QUIZ 7 successfully to complete this QUIZ.
Then, you may proceed to take the QUIZ.
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Solution 8: Supervised ML model Part 1
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Getting started: Logistic regression ML model
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Decision Tree classification ML model
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Random Forest classification ML model
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K Nearest Neighbours classification ML model
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LightGBM classification ML model
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QUIZ 9: Supervised ML model Part 2
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 5. If not, please ensure that you have successfully completed the instructions given in the lecture. Additionally, you have to complete the QUIZ 1, QUIZ 2, QUIZ 3, QUIZ 4, QUIZ 5, QUIZ 6, QUIZ 7 and QUIZ 8 successfully to complete this QUIZ.
Then, you may proceed to take the QUIZ.
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Solution 9: Supervised ML model Part 2
