Data Science is an interdisciplinary field that uses scientific methods, algorithms, processes, and systems to extract knowledge and insights from structured and unstructured data.
Core Components:
1. Data Collection: Gathering data from multiple sources (e.g., databases, APIs, sensors).
2. Data Cleaning: Handling missing values, duplicates, outliers, and errors.
3. Exploratory Data Analysis (EDA): Summarizing main characteristics of the data using statistics and visualization.
4. Feature Engineering: Creating relevant input variables for models.
5. Modeling: Applying machine learning/statistical models to make predictions or classifications.
6. Evaluation: Testing model accuracy, precision, recall, etc.
7. Deployment: Integrating the model into a production environment.
8. Monitoring: Ensuring model performance over time.
a. Key Concepts
b. Statistics & Probability
c. Machine Learning
d. Data Visualization
e. Big Data Technologies
f. Data Ethics and Privacy
Python Statistics for Data Science
The Python Statistics for Data Science course is designed to equip learners with the knowledge and practical skills needed to perform statistical analysis and extract insights from data. Through hands-on coding exercises and real-world examples, you’ll explore probability, hypothesis testing, regression, and much more. Whether you’re beginning your data science journey or aiming to sharpen your analytical skills, this course is your stepping stone to success in the data-driven world.
Course Objectives:
a. By the end of this course, you will be able to:
b. Understand key statistical concepts relevant to data science
c. Perform hypothesis testing and draw meaningful conclusions
d. Calculate and interpret statistical parameters like mean, median, mode, and entropy
e. Apply probability theory and Bayes Theorem
f. Visualize data and statistical distributions using Python
g. Build a foundation in NumPy, Pandas, and Matplotlib
h. Integrate Python with databases and visualize data using Tableau
i. Work on a real-time project to apply everything you’ve learned
Course Modules:
Module 1: Understanding the Data
Goal: Learn to identify, sample, and summarize different types of data using key statistical parameters.
Learning Outcomes:
a. Differentiate between types of data and variables
b. Understand population vs sample
c. Explore common sampling techniques
d. Use numerical and statistical parameters to describe data
Topics Covered:
a. Introduction to Data Types
b. Variable Types and Their Uses
c. Population vs Sample
d. Sampling Techniques
e. Numerical Parameters (Mean, Mode, Median)
f. Sensitivity
g. Information Gain & Entropy
h. Data Representation Techniques
Hands-On:
a. Estimating Mean, Median, and Mode using Python
b. Calculating Information Gain and Entropy in Python
Module 2: Probability and Its Uses
Goal: Understand and apply probability to real-world data science problems.
Learning Outcomes:
a. Learn probability rules
b. Explore dependent and independent events
c. Use Bayes Theorem for conditional probability
d. Understand probability distributions and Central Limit Theorem
Topics Covered:
a. Uses and Need for Probability
b. Bayesian Inference
c. Density Concepts
d. Normal Distribution
e. Central Limit Theorem
Hands-On:
a. Calculating probabilities using Python
b. Implementing Conditional, Joint, and Marginal Probabilities
c. Plotting a Normal Distribution Curve in Python
Module 3: Statistical Inference
Goal: Learn how to draw conclusions from data using formal statistical methods.
Topics Covered:
a. Hypothesis Testing
b. Parametric and Non-Parametric Tests
c. Experimental Design
d. A/B Testing
Module 4: Python for Data Science
Goal: Develop hands-on skills with Python libraries and tools used in statistical analysis and machine learning.
Topics Covered:
a. Introduction to NumPy, Pandas, and Matplotlib
b. Data Preparation and Cleaning
c. Exploratory Data Analysis
d. Basics of Machine Learning
i. Supervised Learning
ii. Unsupervised Learning
e. Introduction to Time Series Analysis
Module 5: Data Integration and Visualization
Goal: Learn how to connect Python with databases and visualize insights using Tableau.
Topics Covered:
a. Connecting Python to Databases
b. SQL Queries in Python
c. Data Visualization in Tableau
Real-Time Project
Put your skills to the test by working on a real-world data project, involving:
a. Data Collection & Cleaning
b. Exploratory Data Analysis
c. Statistical Modeling & Hypothesis Testing
d. Machine Learning Model Implementation
e. Dashboard Creation with Tableau
Course Format:
Mode: Online / Offline / Hybrid
Level: Beginner to Intermediate
Duration: Flexible (Suggested: 6-8 weeks)
Tools Used: Python, Jupyter Notebook, Tableau, SQL
Prerequisites :
a. Basic understanding of Python (helpful but not mandatory)
b. Curiosity to learn and explore data
Who Should Enroll?
a. Aspiring Data Scientists
b. Business Analysts
c. Students in STEM fields
d. Professionals transitioning into Data Science