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Data Science with Python - Training & Certification
Rated 4.5/5 based on 757 customer reviews

Data Science with Python - Training & Certification

Expand your learning skill set by mastering Python

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Modes of Delivery

Online Classroom

Collaborative, enriching virtual sessions, led by world class instructors at time slots to suit your convenience.

Classroom

Our classroom training provides you the opportunity to interact with instructors and benefit from face-to-face instruction.

Team/Corporate Training

Our Corporate training is carefully structured to help executives keep ahead of rapidly evolving business environments.
Group Discount: 10.00% for 2 people 15.00% for 3 to 4 people 20.00% for 5 and above people

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

Curriculum

Module 1- Intro to Data Science

Learning Objectives: Get an idea of what is data science. Why data science is "Rosy" or "Handy" or "Fascinating"
Get acquainted with various analysis and visualization tools used in  data science

Topics

  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Projects
  • Data Science Tools & Technologies

Hands-on:  No hands-on

Module 2- Mastering Python

Learning Objectives:  Learn how to install Python distribution - Anaconda Learn basic data types, strings & regular expressions
Data structures that are used in Python
Learn all about loops and control statements in Python
Write user-defined functions in Python. Learn about Lambda function. Learn object oriented way of writing classes & objects
Learn how to import datasets into Python. Also learn how to write output into files from Python
Manipulate & analyze data using Pandas library. Learn generating insights from your data
Use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization
Hands-on session on a real-life case study

Topics

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data
  • Case Study

Hands-on:

  • Know how to install python distribution like anaconda and other libraries
  • Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects
  • write python code to import dataset into python notebook
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset       
Module 3- Probability & Statistics

Learning Objectives:  Visit basics like mean (expected value), median and mode
Distribution of data in terms of variance, standard deviation and interquartile range
Basic summaries about the data and the measures. Together with simple graphics analysis
Basics of probability with daily life examples
Marginal probability and its importance with respective to datascience
Learn baye's theorem and conditional probability
Learn alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value,

Topics

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

Hands-on: Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Module 4- Advanced Statistics & Predictive Modeling - I

Learning Objectives:  Analysis of Variance and its practical use
Linear Regression with Ordinary Least Square Estimate to predict a continuous variable. It covers strong concepts, model building, evaluating model parameters, measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization
Real Life Case Study with Linear Regression
Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. Covers techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion
Real-Life case study with PCA & FA

Topics

  • ANOVA
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA

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

Module 5- Advanced Statistics & Predictive Modeling - II

Learning Objectives: Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value
Real Life Case Study with Binomial Logistic Regression
KNN Algorith for Classification Problem. Covers techniques that are used to find the optimum value for K
Real Life Case Study with KNN
Decision Trees - for regression & classification problem. Covers both Classification & regression problem. Candidates get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID
Real Life Case Study with Decision Tree

Topics

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree

Hands-on:

  • 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
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics
  • Wine comes in various style. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees)
Module 6- Time Series Forecasting

Learning Objectives: Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data
Real Life Case Study with ARIMA

Topics

  • 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:

  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data
  • Write python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smooting constants.
  • Write python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
    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.
Module 7- Capstone Project

Learning Objectives: An industry mentor guided group project to handle a real-life project. The same way you would execute a data science project in any business problem

Topics

  • Industry relevant capstone project under experienced industry-expert mentor

Hands-on: Project to be selected by candidates.

Projects

Covers Exploratory Data Analysis, Linear Regression, Logistic Regression, Decision Tree, Time Series Forecasting, Recommender Engines, Text Mining, ANN, SVM, K means Clustering, Ensemble Machine Learning Techniques

Key Features

42 hours of Instructor led Training
Interactive Statistical Learning with advanced Excel
Comprehensive Hands-on with Python
Covers Advanced Statistics and Predictive Modeling
Learn Supervised and Unsupervised Machine Learning Algorithms

Our Students See All

Attended a 2 day weekend course by Knowledgehut for the CSM certification. The instructor was very knowledgeable and engaging. Excellent experience.

Attended workshop in April 2018

The CSPO Training was awesome and great. The trainer Anderson made all the concepts look so easy and simple. Using his past experience as examples to explain various scenarios was a plus. Moreover, it was an active session with a lot of participant involvement which not only made it interactive but interesting as well. Would definitely recommend this Training.

Attended workshop in July 2018

Great course. An interesting and interactive session to better understand how to succeed in formulating a business case and how to present it effectively.

Attended workshop in May 2018

The training was very interactive and engaging with the attendees.

Attended workshop in June 2018
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Jin Shi

Director at Timber creek Asset Management from Toronto, Canada
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Richard Dsouza

Business Analyst at Valtech from Bangalore, India
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Wily Salim

Services Project Engineer at Lendlease from Sydney, Australia
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Anish Maidh

Senior Project Manager at Telstra from Melbourne, Australia

Frequently Asked Questions

Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.

Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.

  • 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 datascience techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.

There are no restrictions but participants would benefit if they have basic 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 Python and data science experts who have years of industry experience.

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

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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