Introduction to Data Science Course

Get a foundational overview of Data Science principles and concepts

  • 24 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Get introduced to Supervised and Unsupervised Machine Learning Techniques
  • Get Free E-learning Access to 100+ courses

Description

This foundational course provides a high-level overview of essential Data Science areas. A basic understanding of Data Science from business and technology perspectives is provided, along with an overview of common benefits, challenges, and adoption issues.In this course, you will learn the foundations for Data Science and also learn to use Python - a powerful open source tool. You will come across interesting concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression & classification modeling techniques and get introduced to machine learning. The course end project and interview prep will make you industry ready. 

Named the sexiest career of the 21st century by none other than the Harvard Business Review, the demand for Data Scientists is rapidly increasing year-on-year. It has been proven that data scientists earn base salaries up to 36% higher than other predictive analytics professionals.  Glassdoor reports that the national average salary for a Data Scientist is $1,39,840 in the United States.

KnowledgeHut’s Data Science Foundation course will helps freshers and seasoned professionals alike to gain a deep understanding of the subject and advance your career.

What You Will Learn

Prerequisites
  • Elementary programming knowledge
  • Familiarity with statistics

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Who should Attend?

  • Those interested in data science who want to learn essential data science skills
  • Those looking for a more robust, structured data science learning program
  • Data Analysts, Economists, or Researchers working with large datasets
  • Software or Data Engineers interested in learning basics of quantitative analysis

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objectives:

Get an idea of what is data science. Get acquainted with various analysis and visualization tools used in data science.

Topics Covered:

  • Introduction to Data Science
  • Analytics Landscape
  • Life Cycle of a Data Science Projects
  • Data Science Tools & Technologies

Hands-on: No hands-on

Learning Objectives:

In this module, you will get an overview of basics like mean (expected value), median and mode and will also learn about distribution of data in terms of variance, standard deviation and interquartile range. Get basic summaries about the data and its measures, together with simple graphics analysis.

Go on to explore the basics of probability with daily life examples. Learn about Marginal probability and its importance with respect to data science. Learn Baye's theorem and conditional probability, and alternate and null hypotheses with various types of errors.

Topics Covered:

  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing

Hands-on:

Learn to implement statistical techniques with Microsoft Excel.

Learning Objectives:

This module, which covers the basics of Python, teaches how to install Python distribution - Anaconda and gives an understanding of the basic data types, strings & regular expressions.

Topics Covered:

  • Install Anaconda
  • Data Types & Variables
  • String & Regular Expressions

Learning Objectives:

Understand the various Data structures that are used in Python.

Topics Covered:

  • Python list
  • Python dictionaries
  • Python set
  • Python tuple
  • Comprehensions

Hands-on:

Write Python Code to understand and implement Python Data Structures.

Learning Objectives:In this module, you will learn all about loops and control statements in Python.

Topics Covered:

  • For Loop
  • While Loop
  • Break Statement
  • Next Statements
  • Repeat Statement
  • if, if…else Statements
  • Switch Statement

Hands-on:

Write Python Code to implement loop and control structures in R.

 Learning Objectives:

Here you will learn to write user-defined functions in Python, including Lambda function. Also learn the object oriented way of writing classes & objects.

Topics Covered:

  • Writing your own functions (UDF)
  • Calling Python Functions
  • Functions with Arguments
  • Calling Python Functions by passing Arguments
  • Lambda Functions
  • Classes & Objects

Hands-on:

Write Python Code to create your own custom functions without or with arguments. Know how to call them by passing arguments wherever required.

Learning Objectives:

Learn how to import datasets into Python. Also learn how to write output into files from Python.

Topics Covered:

  • Reading files with Python
  • Writing files from Python
  • Reading files using Pandas library
  • Saving Data using Pandas library

Hands-on:

Write Python Code to read and write data from/to Python.

Learning Objectives:

Learn to manipulate & analyze data using Pandas library. Learn how to generate insights from your data.

Topics Covered:

  • Clean & Prepare Datasets
  • Manipulate DataFrame
  • Summarize Data
  • Churn Insights from Data

Hands-on:

Write Python code to manipulate data frames and churn insights using various Python libraries.

Learning Objectives:

You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization.

Topics Covered:

  • Charts using Matplotlib
  • Charts using Seaborn
  • Charts using ggplot

Hands-on:

Use Python visualization libraries like Matplotlib, Seaborn & ggpplot.

Learning Objectives:

This module analyses Variance and its practical use, covering strong concepts, model building, evaluating model parameters, measuring performance metrics on Test and Validation set. You will use Linear Regression with Ordinary Least Square Estimate to predict a continuous variable. Further you will learn to enhance model performance by means of various steps like feature engineering & regularization.

Along the way, you will learn about Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis, including methods to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. You will be able to cement the concepts learnt through real life case studies with Linear Regression and PCA & FA.

Going further, you will explore Binomial Logistic Regression for Binomial Classification Problems, including evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value. You will work with a real-life case study with Binomial Logistic Regression.

Next, you will learn about KNN Algorithm for Classification Problem, including techniques that are used to find the optimum value for K. You will see a real-life case study with KNN Decision Trees, to help you understand regression & classification problems. At the end of this module you will have working knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID, among others.

Topics Covered:

  • ANOVA
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA
  • Logistic Regression (MLE)
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree

Hands-on: 

  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices. 
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.
  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank to be proactive in collecting dues.
  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

In this module, you will understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data; also work with the Exponential Smoothing Model and know when to use the same.

You will know how to use Holt's model when your data has Constant Data, Trend Data and Seasonal Data and learn how to select the right smoothing constants for each set of circumstances.Finally, you will use Autoregressive Integrated Moving Average Model for building a Time Series Model and carry out a real-life case study with ARIMA.

Topics Covered:

  • Understand Time Series Data
  • Visualizing TIme Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA
  • Case Study: Time Series Modeling on Stock Price

Hands-on:

Work on a dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

This module gives you a good understanding of the fundamental issues and challenges of machine learning. You will learn types of machine learning such as Supervised and Unsupervised Learning and get an understanding of the strengths and weaknesses of many popular machine learning approaches.

Learn to appreciate the underlying relationships within and across Machine Learning algorithms and the paradigms of supervised and unsupervised learning. Cement your understanding of all these concepts with a case study using Scikit Learn libraries for data manipulation & pre-processing.

Topics Covered:

  • What is Machine Learning?
  • Supervised Learning
  • Unsupervised Learning
  • Using Scikit-learn
  • Scikit-learn classes
  • Case Study: Machine Learning Algorithm

Hands-on:

Use complex datasets to manipulate, prepare and preprocess data for model building exercise. Analyze and treat missing values using various missing value imputation strategies.

Projects

House Price Prediction using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices

Predict credit card defaulter using Logistic Regression

With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.

Read More

Predict quality of Wine using Decision Tree

Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Predict Stock Prices using ARIMA

Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Note:These were the projects undertaken by students from previous batches.  

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Faqs

The Course

That big data has truly arrived is common knowledge but the question that is plaguing most organizations is how to manage and effectively make use of this data. Expert data scientists who can mine this data and provide useful insights that will help in the growth of business and organizations are therefore much in demand.


The 5th-annual Burtch Works Study: Salaries of Data Scientists puts median compensations for individual contributors in a range of $95,000 at level 1 (0-3 years of experience) to $165,000 at level 3 (9+ years). Managers can earn $145,000 at level 1 (1-3 reports) to $250,000 at level 3 (10+ reports).

KnowledgeHut’s course content is divided into a series of modular sections, each of which is accompanied by one or more hands-on exercises and will give you skills that are immediately deployable at work. Join now and become one among the elite group of data scientists who command high salaries and respect all over the world!

  • Get advanced knowledge of data science and how to use them in real life business
  • Understand the statistics and probability of Data science
  • Get an understanding of data collection, data mining and machine learning
  • Learn tools like Python

By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as data analysts. 

Tools and Technologies used for this course are

  • Python
  • MS Excel

There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Data science and Python experts who have years of industry experience.

Finance Related

Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details: Refund Policy.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

Certification FAQs

Data Science Foundation

Introduction to Data Science’ is a foundation course that will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields and learn how data analysis can help you make data driven decisions. You’ll cover Advanced Statistics and Predictive Modeling and get introduced to Supervised and Unsupervised Machine Learning Techniques.

Data Science is a promising career for many different reasons:

  • There’s a high and growing demand for Data Scientists. LinkedIn estimates 11.5 million jobs by the year 2026. 
  • Data Science is used across various industries including retail, lifesciences, banking, pharmaceuticals, technology, aerospace, manufacturing amongst several others. Its applications keep growing with time.
  • Glassdoor estimates an average annual salary of $ 116,100 for a Data Scientist.

Data Science is changing the world in several ways. 

  • Many products and services which we take for granted today are driven by data science. We’re able to book a ride at a justified price, we receive packages within a day, movie recommendation engines help us discover new movies – these are just a few.  
  • Several big challenges are being tackled leveraging data science, such as preventing global warming and  finding ways to control the spread of pandemics.  
  • Data Science gives companies the opportunity to closely analyze buyer behaviours. This helps companies serve their customers better and the company benefits from making more data driven decisions.   

At the heart of all of this is the Data Scientist extracting and analyzing relevant data and coming out with actionable insights.

Most certainly. If you’re looking to break into a Data Science career, you’ll find exactly what you need in this foundational Data Science course. You’ll cover all the key aspects involved in it and how they work together. This will also serve as a firm foundation to build on with more advanced courses in Data Science. 

Data Science has its origin in statistics. Its importance increased with the emergence of Big Data. The trend continues where decision makers now have access to massive amounts of data and they will keep requiring Data Scientists to make sense of it. The roles within the umbrella of Data Science will keep getting specialized like Data Analyst, Data Engineer, Data Architect and so on. Automation will play a role in cutting down of some tasks and the Data Scientist will spend more time on interpreting the data and finding insights from it. 

Data Science helps decision makers in a variety of ways:

  • Help identify patterns in unstructured data
  • Make scientific predictions based on data
  • Categorizing data automatically
  • Spot unusual patterns or anomalies
  • Find correlations and examine causes and effects
  • Manage large quantities of data 

The importance of Data as a valuable resource has been rapidly increasing. Businesses today gather massive amounts of data and all of this needs to be managed. It could be used in a way where the data can provide insights and help in taking data driven decisions.  All top companies are building or expanding their teams working on data science. This has led smaller companies to leverage data science as well.

Data Science helps organizations by:

  • Finding patterns in buyer behavior.
  • Enabling faster and better decision making leading to improved efficiency.  
  • Letting marketing and sales team understand the target audience in a more concrete way.

Introduction to Data Science is a way to develop a solid foundational knowledge about data science and its scope. It takes you through concepts of statistics, It also gives you some hands on experience in working with Python, and a range of other tools that Data Scientists typically use.

There are no prerequisites for this course. Anybody interested in learning Data Science can get enrolled in this course.

All big companies across industries use Data Science. These are some of the most prominent:

  • Amazon 
  • American Express 
  • BDO 
  • Capital One 
  • General Electric 
  • Miniclip 
  • Netflix 
  • Next Big Sound 
  • Starbucks 
  • T-Mobile 

The demand for Data Science has only increased. There is a massive skill gap with most companies trying to expand their Data Science Teams. There is a high demand for professionals in the data science space who can program, manipulate, and store data. 

Data Science is not hard, it does require a variety of skill depending on the specific role you wish to do. It also helps to keep updating these skills. There is a high demand for qualified Data Scientists. As long as you have an interest in technology and data, Data Science will be easy.

Learn Data Science Foundation

In this course you will learn the basics of Data Science and you will also get to learn and practice how to apply what you learned. The following topics will be explored:

  • Foundation of Data Science 
  • Probability and Statistics 
  • Working with Python 
  • Data Visualization 
  • Predictive Modeling 
  • Times Series Forecasting 
  • Machine Learning Introduction 

This course gives you a solid foundation to a career in Data Science. It also teaches you statistical concepts, data modeling and how to work with python. It has all the necessary elements that you will require to start a career or further your career in data science. You will also get hands-on experience of working on what you learn.

There are plenty of institutions offering a wide variety of courses in Data Science and also courses for learning tools and skills that help in Data Science. You should look for a course that can take you from the basics of statistics and take you to a level where you are in a position to practically use the concepts and tools that you learn.

Data Science has its origins in statistics. To get a good foundational understanding of statistics would be the right place to start. From there you can decide which tool or skill is more necessary for your development as a data scientist, or you could undergo a course that covers the basics and lets you become a fully capable data scientist.

The fundamentals of Data Science can be learnt in a matter of weeks. How quickly you can get a foundation to Data Science would depend on your background and previous experience. A minimum of one month would be required to get a foundational knowledge. To become an expert involves continuous learning since technology keeps evolving and practice is required to improve one's skills.

Yes, most courses on Data Science are online nowadays. As long as you have an instructor with whom you can clear doubts, learning Data Science online will not be a problem. 

There are no prerequisites to this course. This course starts from the basics of Data Science.

Data Science Certification

You get a certification of completion on completing this course.

Your instructors are professional having the real-world experience of working on Data Science and Python.

Data Scientist

All major companies are looking to hire experts in Data Science, following are the prominent ones:

  • Accenture 
  • Amazon 
  • Apple 
  • Facebook 
  • Fidelity Investments 
  • Google 
  • Intel 
  • Microsoft 
  • Twitter 
  • PayPal 

The Average annual Salary for a Data Scientist is ₹10 Lakhs p.a. in India and $94,280 in the US.

On an average a Date Scientist earns $94,280 in the US and ₹10 Lakhs p.a. in India.

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