Data Science Tutorial

Data is ubiquitously present and continuously growing at an exponential rate. In fact, a staggering 90% of the world's data has been generated within the recent years alone. This overwhelming volume of data poses a significant challenge for organizations, as they strive to extract valuable insights necessary for making informed and improved business decisions. In response to this challenge, the field of Data Science has emerged, amalgamating methodologies from machine learning, artificial intelligence (AI), and statistics.

Additionally, Data Science incorporates cutting-edge technologies that enable the efficient processing and analysis of vast and intricate datasets, which are characterized by their complexity and dynamic nature. By harnessing the power of Data Science, organizations can unlock the hidden potential within their data, gaining invaluable insights to drive innovation, enhance decision-making processes, and achieve a competitive edge in the ever-evolving business landscape.

Countless organizations and government departments worldwide depend on the utilization of big data to thrive and effectively cater to their customers' needs. Developing proficiency in the field of data science necessitates beginning with the foundational aspects of a programming language and gradually progressing towards the implementation of practical projects.


Data science tutorial

This series of online DataScience tutorials has been designed to facilitate your journey in the realm of data science, enabling you to advance and showcase your newfound skills in this rapidly evolving domain. By engaging with these tutorials, you will acquire the knowledge and expertise essential for navigating the dynamic world of data science, enhancing your capabilities, and making a noteworthy impact in the field.


  Data Science Introduction
  1. What is Data Science?
  2. Need for Data Science
  3. Applications of Data Science
  1. Types of data science jobs
  2. What is Required to Learn Data Science?
  3. Data Science Process

  Why learn Python for Data Science?
  1. Python Getting Started
  2. Python Fundamentals
  3. Python Control Flow Statements
  1. Python Data Structures
  2. Python File Access

  Statistics for Data Science
  1. Measures of Central Tendency
  2. Measures of the Spread of Data
  3. Relationship Between Variables
  1. Statistical Distributions
  2. Hypothesis Testing
  3. Regression Algorithms
  4. Classification Algorithms
  5. Model Evaluation and Model Validation

  Data Science Libraries - Python
  1. NumPy - Numerical Python
  2. pandas - Python Data Analysis Library
  3. SciPy : Scientific Python
  1. Matplotlib: Python plotting
  2. Seaborn: statistical data visualization
  3. Scikit-Learn : Python Machine Learning

  Pandas DataFrame operations
  1. Creating an empty Pandas DataFrame
  2. How to Check if a Pandas DataFrame is Empty
  3. How to check if a column exists in Pandas Dataframe
  1. How to delete column from pandas DataFrame
  2. How to select multiple columns from Pandas DataFrame
  3. Selecting multiple columns in a Pandas dataframe based on condition
  4. Selecting rows in pandas DataFrame based on conditions
  5. How to Drop rows in DataFrame by conditions on column values
  6. Rename column in Pandas DataFrame
  7. Get a List of all Column Names in Pandas DataFrame
  8. How to add new columns to Pandas dataframe?
  9. Change the order of columns in Pandas dataframe
  10. Concatenate two columns into a single column in pandas dataframe
  11. How to count the number of rows and columns in a Pandas DataFrame
  12. Use a list of values to select rows from a pandas dataframe
  13. How to iterate over rows in a DataFrame in Pandas
  14. How to drop rows/columns of Pandas DataFrame whose value is NaN
  15. How to Export Pandas DataFrame to a CSV File
  16. Convert list of dictionaries to a pandas DataFrame
  17. How to set a particular cell value in pandas DataFrame

  Pandas DataFrame Examples
  1. Pandas DataFrame: GroupBy Examples
  2. Pandas DataFrame Aggregation and Grouping
  3. How to Sort Pandas DataFrame
  1. Pandas DataFrame: query() function
  2. Finding and removing duplicate rows in Pandas DataFrame
  3. How to Replace NaN Values With Zeros in Pandas DataFrame
  4. How to read CSV File using Pandas DataFrame.read_csv()
  5. How to Convert Pandas DataFrame to NumPy Array
  6. How to shuffle a DataFrame rows
  7. Import multiple csv files into one pandas DataFrame
  8. Create new column in DataFrame based on the existing columns
  9. New Pandas dataframe column based on if-else condition
  10. How to Convert a Dictionary to Pandas DataFrame
  11. Rename Pandas columns/index names (labels)
  12. Check for NaN Values : Pandas DataFrame

  Python Statistical Data Analysis
  1. Measuring Central Tendency : Mean, Median, Mode
  2. How to find Inter-Quartile Range (IQR)
  3. Standard Deviation and Variance
  1. Correlation Between Variables
  2. Mean Squared Error and Root Mean Squared Error
  3. R-Squared |Coefficient of Determination
  4. Adjusted R-Squared

  Machine Learning Algorithms | Python
  1. Simple Linear Regression | Python Data Science
  2. Multiple Linear Regression | Python Data Science
  3. Ordinary Least Squares Regression | Python Data Science
  1. Polynomial Regression | Python
  2. Logistic Regression | Python Machine Learning
  3. K-Nearest Neighbor(KNN) | Python Machine Learning
  4. Decision Tree in Machine Learning | Python
  5. Random Forest | Python Machine Learning
  6. Support Vector Machine | Python Machine Learning

  Python Data Science Errors
  1. ImportError: No module named pandas
  2. What is SettingWithCopyWarning?
  3. UnicodeDecodeError while reading CSV file
  1. How to fix CParserError: Error tokenizing data
  2. ValueError: cannot reindex from a duplicate axis
  3. How to fix "Unnamed: 0" column in a pandas DataFrame
  4. ValueError: cannot convert float NaN to integer
  5. ValueError: Unknown label type: 'unknown'
  6. ValueError: Length of values does not match length of index
  7. ValueError: The truth value of an array with more than..
  8. Attributeerror: 'dataframe' object has no attribute 'concat'

  Data Science Interview Questions
  1. Data Science Interview Questions (Part 2)
  2. Machine Learning Interview Questions
  3. Statistics Interview Questions
  1. Scikit-learn Interview Questions