HRDF Approved Course

Introduction to Data Science Training

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
Group Discount

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

3 Months FREE Access to all our E-learning courses when you buy any course with us

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 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 modules 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 characterisitcs of a time series data. We will use ARIMA to predict the stock prices.

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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: http://www.knowledgehut.com/refund

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

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