Getting Started with Data Science
What is Data Science? Why has it become so popular recently? What are some of the popular data science applications? And more importantly, how can you get started with learning data science from scratch?
Are you looking for the answer to these questions? Frustrated by the lack of structured data science learning? You’ve come to the right place!
Data science is ubiquitous right now. Organizations are splurging to integrate data science solutions in their daily processes. It’s a great time to learn data science and get ready for your first industry role!
This course, curated by experienced data science instructors and experts at Analytics Vidhya, will cover the core concepts you need to know to crack data science interviews and become a data scientist!
Why pursue Data Science:
- Data Science is ubiquitous! It is the hottest field in the industry right now
- Data Scientists are one of the most demanded professionals
- There are so many data science algorithms to build predictive models, such as linear regression, logistic regression, decision trees and random forests. Keep learning, keep growing!
- The potential of data science is limitless - spanning across industries, roles and functions.
Understand What Data Science is
Applications of Data Science
Data Science Terminologies
Python for Data Science
Core Statistics for Data Science
Introduction to Machine Learning Algorithms
Hands-on examples and multiple real-world industry-relevant data science projects
- Course Handouts
- Brief Introduction to Python
- Introduction to Python test
- Installation steps for Mac
- Installation steps for Linux
- Installation steps for Windows
- Theory of Operators
- Understanding Operators in Python
- Operators test
- Understanding variables and data types
- Variables test
- Variables and Data Types in Python
- Understanding Conditional Statements
- Implementing Conditional Statements in Python
- Conditional Statements test
- Understanding Looping Constructs FREE PREVIEW
- Implementing Looping Constructs in Python
- Looping Constructs test
- Understanding Functions
- Implementing Functions in Python
- Functions test
- A brief introduction to data structure
- Data Structure test
- Understanding the concept of Lists
- Understanding the concept of Lists-Reference
- Lists test
- Implementing Lists in Python
- Understanding the concept of Dictionaries
- Implementing Dictionaries in Python
- Dictionaries test
- Understanding the concept of Standard Libraries
- Libraries test
- Reading a CSV File in Python - Introduction to Pandas
- Reading a CSV file in Python - Implementation
- Reading a csv file in Python test
- Understanding dataframes and basic operations
- DataFrames and basic operations test
- Reading dataframes and conduct basic operations in Python
- Reading dataframes and conduct basic operations in Python Test
- Indexing a Dataframe
- Indexing DataFrames test
- Understanding Regular Expressions
- Quiz: Regular Expressions
- Regular Expressions in Python
- Quiz: Regular Expressions in Python
- Python Coding Challenge
- Introduction to statistics
- Mode of the data
- Mode test
- Understanding the various variable types
- Understanding Variable Types test
- Mean of the data
- Mean test
- Outliers in the datasets
- Outlier test
- Median of the dataset
- Median test
- Spread of the data
- Spread test
- Variance of the data
- Variance test
- Standard Deviation of the data
- Standard Deviation test
- Frequency Tables
- Frequency Tables test
- Histograms test
- Introduction to Probability
- Introduction to probability test
- Calculating Probabilities of events
- Calculating Probabilities test
- Bernoulli Trials and Probability Mass Function
- Bernoulli Trials and PMF test
- Probabilities for Continuous Random Variables
- Probabilities for continuous random variable test
- The Central Limit Theorem
- Central Limit Theorem test
- Properties of the Normal Distribution
- Properties of Normal distribution test
- Using the Normal Curve for Calculations
- Normal Curve for calculations test
- Z score Part 1
- Understanding the Z tables
- Z scores test
- Z score part 2
- Introduction to Inferential Statistics
- Introduction to Inferential Statistics test
- Short Review
- Review test
- Mean Estimation
- Confidence Interval and Margin of Error
- CI and Margin of error test
- Introduction to Hypothesis Testing
- Hypothesis testing test
- Steps to perform hypothesis testing
- Directional Non Directional hypothesis
- Directional and Non Directional hypothesis test
- Understanding Errors while Hypothesis Testing
- Errors while Hypothesis testing test
- Understanding T tests
- Understanding T tests - test
- Degree of Freedom
- T-Critical Value
- T-Critical Value Test
- Steps to perform T-Test
- Steps to perform T-Test test
- Conducting One sample T test
- One sample T tests - test
- Paired T tests
- Paired T tests - test
- 2 Sample T tests
- 2 sample T tests - test
- Chi Squared Tests
- Chi squared tests - test
- Correlation test
- Module Test
- Statistics Coding Challenge
- Assignment: Share your learning and build your profile
- Sorting Dataframes
- Merging Dataframes
- Quiz: Sorting and Merging dataframes
- Apply function
- Aggregating data
- Quiz: Apply function and Aggregating data
- Basics of Matplotlib
- Data Visualization using Matplotlib
- Quiz: Matplotlib
- Basics of Seaborn
- Data Visualization using Seaborn
- Quiz: Seaborn
- Introduction to Predictive Modeling
- Predictive Modeling Introduction test
- Types of Predictive Models
- Types of Prediction Models test
- Stages of Predictive Modeling FREE PREVIEW
- Stages of Predictive Modeling test
- Understanding Hypothesis Generation
- Hypothesis Generation test
- Data Extraction
- Data Extraction Test
- Understanding Data Exploration
- Data Exploration Test
- Reading the data into Python
- Reading Data into Python test
- Reading the data into Python : Implementation
- Reading the data into Python : Implementation Test
- Variable Identification
- Variable Identification test
- Variable Identification : Implementation
- Variable Identification : Implementation Test
- Univariate analysis for Continuous Variables
- Univariate analysis for Continuous variables test
- Univariate Analysis for Continuous Variables : Implementation
- Univariate Analysis for Continuous Variables : Implementation Test
- Understanding Univariate Analysis for categorical variables
- Univariate Analysis for categorical test
- Univariate analysis for Categorical Variables : Implementation
- Univariate analysis for Categorical : Implementation Test
- Understanding Bivariate Analysis
- Bivariate Analysis Test
- Bivariate Analysis : Implementation
- Bivariate Analysis : Implementation Test
- Understanding and treating missing values
- Treating missing values test
- Treating missing values : Implementation
- Treating missing values : Implementation Test
- Understanding Outlier Treatment
- Outlier treatment test
- Outlier Treatment in Python
- Outlier Treatment in Python Test
- Understanding Variable Transformation
- Transforming variables test
- Variable Transformation in Python
- Variable Transformation in Python Test
- Basics of Model Building
- Basics of Model Building test
- Understanding Linear Regression
- Linear Regression test
- Implementing Linear Regression in Python
- Linear Regression Implementation Test
- Understanding Logistic Regression
- Logistic Regression test
- Implementation of logistic Regression
- Logistic Regression Implementation Test
- Understanding Decision Trees
- Understanding Decision Tree Test
- Decision tree - Splitting
- Decision tree splitting criteria
- Decision Tree splitting Test
- Implementation of Decision Tree
- Decision Tree Implementation test
- Introduction to Evaluation Metrics
- Evaluation Metrics Test
- Understanding Confusion Matrix
- Confusion matrix Test
- Accuracy test
- Alternatives of Accuracy
- Alternatives of Accuracy Test
- Precision Recall
- Precision & Recall Test
- Thresholding Test
- AUC ROC
- AUC ROC Test
- Log Loss
- Log Loss Test
- Evaluation Metrics for Regression
- Evaluation metrics for regression Test
- Adjusted R-squared
- Adjusted R-squared Test
- Introduction to Random Forest
- Building a Random Forest
- Hyperparameters of Random Forest
- Implementation of random forest
- Understanding K-means
- K-Means Test
- Implementation of K-Means
- K-Means Implementation Test
- Module Test
- Modeling Coding Challenge
- Assignment: Share your learning and build your profile
- Project 1 - Classification
- Project 2 - Regression
The course covers all the 3 aspects of Data science, i.e Programming, Statistics, and the ML part. It also has 2 final projects to let you practice the newly...Read More
The course covers all the 3 aspects of Data science, i.e Programming, Statistics, and the ML part. It also has 2 final projects to let you practice the newly learned skills. It's a 10/10 from me 👍Read Less
I had been trying to get into data science on my own for some time, but this course provided a very good structure and the hands on experience needed to star...Read More
I had been trying to get into data science on my own for some time, but this course provided a very good structure and the hands on experience needed to start the journey in a simple manner. The lectures are easy to understand and the course covers basics of Python, Statistics and Predictive Modeling.Read Less
Easy going course with hands-on exercises.
Easy going course with hands-on exercises.Read Less
very organized easy to follow course
very organized easy to follow courseRead Less
Kunal is the Founder of Analytics Vidhya. Analytics Vidhya is one of largest Data Science community across the globe. Kunal is a data science evangelist and has a passion for teaching practical machine learning and data science. Before starting Analytics Vidhya, Kunal had worked in Analytics and Data Science for more than 12 years across various geographies and companies like Capital One and Aviva Life Insurance. He has worked with several clients and helped them build their data science capabilities from scratch.
Neeraj is working at Fractal Analytics. Prior to that Neeraj was a data scientist with Analytics Vidhya. He has extensive experience in converting business problems to data problems. He has previously conducted several corporate trainings and is also an avid blogger. He's a graduate of IIT-BHU and will be your instructor for the Python and Modeling modules.
I have no programming experience. Would I need to learn Python to learn data science?
Programming is an essential aspect of being a data scientist or a data science professional. And Python is the market leader in this space. Organizations globally are adopting Python as their go-to language, including big tech firms like Spotify, Netflix, Facebook, among others.
Python consistently ranks top in global data science surveys and its widespread popularity will only keep on increasing in the coming years.
Over the years, with strong data science community support, this language has obtained a dedicated library for data analysis and predictive modelling.
And don’t worry! Python is a very easy language to learn and we cover it from scratch in the course. So you don’t need to have any prior programming knowledge to master Python!
Do I need to know statistics before taking this course?
No! Statistics is the backbone of data science and we understand that. We have designed an entire comprehensive module on statistics which we cover in the course.
We will cover both descriptive statistics and inferential statistics in detail, along with how to implement each concept in Python. And once you’ve learned and practiced statistics concepts, we will then jump to data science modelling.
What kind of projects can I take up after this course?
You can take up a variety of data science projects! Since this covers both regression and classification algorithms, like linear regression, logistic regression and decision trees, you’ll be well equipped to apply your data science and Python skills on real world projects.
We recommend you pick up the projects we’ve curated on the DataHack platform. These projects will hone your data science skills and enhance what you have learned in the Introduction to Data Science course.
Can I add the projects covered in this course in my resume?
Of course! Projects are among the first things a hiring manager or recruiter looks for in a data science resume. The more projects you add, the stronger your chance of landing your dream role.
As mentioned above, you can head to the DataHack platform and pick up projects from there. Practice is key in data science!
Will this course help me clear data science interviews?
This course will help you build a solid base for data science. You will learn a new programming language (Python), the backbone of data science (statistics), and core predictive modeling techniques.
As a next step in your journey to become a data scientist, we recommend taking the below courses to solidify your portfolio and enhance your chances of landing your dream role:
● Applied Machine Learning
● Ace Data Science Interviews
● Structured Thinking for Data Science Professionals
Who should take this course?
This course is designed for people looking to learn data science. We will start by understanding the basic concepts from scratch, and then go on to solve case studies using data science concepts.
When will the classes be held in this course?
This is a self paced course, which you can take any time at your convenience over the 6 months after your purchase.
How many hours per week should I dedicate to complete the course?
If you can put between 6 to 8 hours a week, you should be able to finish the course in 4 to 6 weeks.
Do I need to install any software before starting the course ?
You will get information about all installations as part of the course.
What is the refund policy?
The fee for this course is non-refundable.
Do I need to take the modules in a specific order?
We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it.
Do I get a certificate upon completion of the course?
Yes, you will be given a certificate upon satisfactory completion of the course.
What is the fee for this course?
Fee for this course is INR 7,999
How long I can access the course?
You will be able to access the course material for six months since the start of the course.