Data Science

Master the complete end-to-end data science workflow with this comprehensive course from Ucanly. This intermediate program is designed for learners who want to combine statistics, programming, and machine learning to solve complex problems. You will learn how to ask the right questions, wrangle messy data, and build predictive models to uncover hidden insights and drive impactful, data-driven decisions using Python.

With the expert guidance of Ucanly, you will go beyond individual tools to master the full data science pipeline. You’ll learn to perform rigorous Exploratory Data Analysis (EDA), apply robust statistical methods, and train a variety of machine learning models. This Ucanly course also introduces you to the challenges of working with Big Data and emphasizes the crucial skill of communicating your findings effectively to stakeholders.

By the end of this project-driven course, you will have completed a full-scale data science project—from formulating a hypothesis to deploying a model and presenting your results—equipping you with a portfolio-ready case study that demonstrates your analytical prowess.

Execute a full, end-to-end data science project from hypothesis to final presentation.
Apply key statistical concepts and hypothesis testing to validate your findings.
Perform in-depth Exploratory Data Analysis (EDA) to uncover insights.
Build, evaluate, and tune a variety of machine learning models for prediction.
Understand the landscape of Big Data technologies like Spark and Hadoop.
Master the art of data storytelling to effectively communicate your results to any audience.
Build a strong portfolio of projects to showcase your data science skills.

Live Q&A and Project Mentoring Sessions with Instructors
On-demand video lectures
Real-world, complex datasets for your projects
Downloadable Python notebooks, code templates, and statistical guides
Quizzes and challenging assignments to solidify your knowledge
Lifetime access to course materials and future updates
Certificate of Completion from Ucanly

Course Content

Module 1: The Data Scientist’s Mindset (The Ucanly Framework)
This module, curated by Ucanly, establishes the problem-solving approach and critical thinking skills that define a successful data scientist.

Lesson 1.1: Course Introduction: What Does a Data Scientist Actually Do?
Lesson 1.2: The Data Science Lifecycle: From Business Problem to Deployed Solution
Lesson 1.3: Asking the Right Questions: Formulating a Hypothesis
Lesson 1.4: The Python Data Science Ecosystem (Pandas, NumPy, Scikit-learn, Matplotlib)
Lesson 1.5: Review of Your Data Science Environment Setup

Module 2: Applied Statistics for Data Science
Build the essential statistical foundation needed to understand your data and validate your models.

Module 3: Data Wrangling and Exploratory Data Analysis (EDA)
Learn to find, clean, and explore datasets to uncover initial patterns and prepare for modeling.

Module 4: Machine Learning in Practice (A Ucanly Deep Dive)
Apply and evaluate a range of machine learning models to solve real-world prediction and classification problems, with guidance from Ucanly.

Module 5: Introduction to Big Data Technologies
Get a high-level understanding of the tools and techniques used when data becomes too large for a single machine.

Module 6: Communicating Data Insights
Master the crucial "last mile" of data science: presenting your results in a clear, compelling, and actionable way.

Module 7: Advanced Topics and Specializations
Explore a specific, high-impact area of data science to deepen your expertise.

Module 8: Capstone: End-to-End Data Science Project (The Ucanly Challenge)
Integrate all your skills to execute a complete data science project on a dataset of your choice.

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Ucanly

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Price:
Original price was: ₹25,000.00.Current price is: ₹14,500.00.
Instructor:Ucanly
Duration: 60 hours
Lessons:36
Students:0
Level:Beginner

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Course Content

Module 1: The Data Scientist’s Mindset (The Ucanly Framework) This module, curated by Ucanly, establishes the problem-solving approach and critical thinking skills that define a successful data scientist.

Lesson 1.1: Course Introduction: What Does a Data Scientist Actually Do?

Lesson 1.2: The Data Science Lifecycle: From Business Problem to Deployed Solution

Lesson 1.3: Asking the Right Questions: Formulating a Hypothesis

Lesson 1.4: The Python Data Science Ecosystem (Pandas, NumPy, Scikit-learn, Matplotlib)

Lesson 1.5: Review of Your Data Science Environment Setup

Module 2: Applied Statistics for Data Science Build the essential statistical foundation needed to understand your data and validate your models.

Lesson 2.1: Descriptive vs. Inferential Statistics

Lesson 2.2: Probability Distributions (Normal, Binomial, Poisson)

Lesson 2.3: Hypothesis Testing: A/B Testing and p-values

Lesson 2.4: Correlation vs. Causation: A Critical Distinction

Lesson 2.5: Project: Running a Statistical Analysis on a Business Dataset

Module 3: Data Wrangling and Exploratory Data Analysis (EDA) Learn to find, clean, and explore datasets to uncover initial patterns and prepare for modeling.

Lesson 3.1: Advanced Data Cleaning Techniques (Handling Outliers, Duplicates, Missing Values)

Lesson 3.2: Feature Engineering: Creating New Variables from Existing Data

Lesson 3.3: The Art of Exploratory Data Analysis (EDA)

Lesson 3.4: Advanced Data Visualization for Storytelling

Lesson 3.5: Live Session: Live EDA on a Complex, Unseen Dataset

Module 4: Machine Learning in Practice (A Ucanly Deep Dive) Apply and evaluate a range of machine learning models to solve real-world prediction and classification problems, with guidance from Ucanly.

Lesson 4.1: A Review of Supervised Learning: Regression and Classification Models

Lesson 4.2: Model Selection and Cross-Validation Techniques

Lesson 4.3: Building Ensemble Models (Random Forests, Gradient Boosting)

Lesson 4.4: Introduction to Unsupervised Learning: Clustering and Anomaly Detection

Lesson 4.5: Project: Building a High-Performance Classification Model

Module 5: Introduction to Big Data Technologies Get a high-level understanding of the tools and techniques used when data becomes too large for a single machine.

Lesson 5.1: What is Big Data? (The 3 Vs: Volume, Velocity, Variety)

Lesson 5.2: Introduction to the Hadoop Ecosystem (HDFS & MapReduce)

Lesson 5.3: Large-Scale Data Processing with Apache Spark

Lesson 5.4: Querying Big Data with Hive and SQL-on-Hadoop

Module 6: Communicating Data Insights Master the crucial "last mile" of data science: presenting your results in a clear, compelling, and actionable way.

Lesson 6.1: Principles of Effective Data Storytelling

Lesson 6.2: Building Interactive Dashboards (Introduction to a tool like Dash or Streamlit)

Lesson 6.3: Presenting to Technical and Non-Technical Audiences

Lesson 6.4: Creating a Professional Data Science Portfolio

Module 7: Advanced Topics and Specializations Explore a specific, high-impact area of data science to deepen your expertise.

Lesson 7.1: Introduction to Time Series Analysis and Forecasting

Lesson 7.2: Recommender Systems 101

Lesson 7.3: Model Deployment Basics: Turning Your Model into an API

Lesson 7.4: Live Session: A Walkthrough of a Specialized Industry Case Study

Module 8: Capstone: End-to-End Data Science Project (The Ucanly Challenge) Integrate all your skills to execute a complete data science project on a dataset of your choice.

Lesson 8.1: Project Scoping and Data Acquisition

Lesson 8.2: In-depth EDA and Feature Engineering

Lesson 8.3: Model Building, Evaluation, and Iteration

Lesson 8.4: Final Report and Presentation of Findings

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