Data Science with Python Training in Gurgaon, India

Get hands-on Python skills and accelerate your data science career

  • Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit
  • Create robust predictive models with advanced statistics
  • Leverage hypothesis testing and inferential statistics for sound decision-making
  • 220,000 + Professionals Trained
  • 250 + Workshops every month
  • 100 + Countries and counting

Grow your Data Science skills

This comprehensive hands-on course takes you from the fundamentals of Data Science to an advanced level in weeks. Get hands-on programming experience in Python that you'll be able to immediately apply in the real world. Equip yourself with the skills you need to work with large data sets, build predictive models and tell a compelling story to stakeholders.

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Highlights

  • 42 Hours of Live Instructor-Led Sessions

  • 60 Hours of Assignments and MCQs

  • 36 Hours of Hands-On Practice

  • 6 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

Data Scientists are in high demand across industries

data-science-with-python-certification-training

Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand data science and Python skills and meet that need.

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The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Exclusive Post-Training Sessions

Six months of post-training mentor guidance to overcome challenges in your Data Science career.

Prerequisites

Prerequisites for the Data Science with Python training program

  • There are no prerequisites to attend this course.
  • Elementary programming knowledge will be of advantage.

Who should attend the Data Science with Python course?

Professionals in the field of data science

Professionals looking for a robust, structured Python learning program

Professionals working with large datasets

Software or data engineers interested in quantitative analysis

Data analysts, economists, researchers

Data Science with Python Course Schedules

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What you will learn in the Data Science with Python course

Python Distribution

Anaconda, basic data types, strings, regular expressions, data structures, loops, and control statements.

User-defined functions in Python

Lambda function and the object-oriented way of writing classes and objects.

Datasets and manipulation

Importing datasets into Python, writing outputs and data analysis using Pandas library.

Probability and Statistics

Data values, data distribution, conditional probability, and hypothesis testing.

Advanced Statistics

Analysis of variance, linear regression, model building, dimensionality reduction techniques.

Predictive Modelling

Evaluation of model parameters, model performance, and classification problems.

Time Series Forecasting

Time Series data, its components and tools.

Skill you will gain with the Data Science with Python course

Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Data distribution: variance, standard deviation, more

Calculating conditional probability via hypothesis testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Binomial Logistic Regression models

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for regression and classification

Visualizing Time Series data and components

Exponential smoothing

Evaluating model parameters

Measuring performance metrics

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Invest in forward-thinking data talent to leverage data’s predictive power, craft smart business strategies, and drive informed decision-making.

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Data Science with Python

What is Data Science

Crowned as the sexiest job of the 21st Century by the Harvard Business Review, Data Science is a field of opportunities. Gurgaon is home to many leading companies, including ZS Associates, Dell, Ericsson, Google, Ibibo India, Ixigo, Expedia, Microsoft, Oracle, Qualcomm, Royal Bank of Scotland, etc. These companies are constantly looking to add data science experts to make sense of their data. Some other reasons why data science is popular are:

  1. Data-driven decision making is the need of the hour. 
  2. Due to the lack of well-trained data scientists, professionals trained in it are offered the highest salary in the tech world.
  3. Data is being collected at an exceptionally high rate, which requires an equal rate of analysis to make the most of it. It is needed to take crucial decisions. 

The skills you need to become a data scientist include the following:

  • Python Coding: Python is one of the most popularly used programming languages. Owing to the versatility as well as the simplicity that Python offers, it takes various formats of data and helps in the processing of this data.
  • R Programming: Knowledge of the analytical tool R programming is an advantage for data scientists to solve any data science problem.
  • Hadoop Platform: It may not be a requirement per se, but is preferred in the job of a data scientist. 
  • SQL database and coding: SQL is a language that is specifically designed to help data scientists access, communicate as well as work on data. It helps form a structure of the database.
  • Machine Learning and Artificial Intelligence: Proficiency in the areas of Machine Learning and Artificial Intelligence is now a prerequisite for the pursuit of a career in Data Science. Familiarise yourself with these topics:
    • Reinforcement Learning
    • Neural Network
    • Adversarial learning 
    • Decision trees
    • Machine Learning algorithms
    • Logistic regression etc.
  • Apache Spark: Apache Spark is a platform for big data computation, not unlike Hadoop. The only difference between the two is that Apache Spark is faster, because of the fact that Hadoop reads and writes to the disk, whereas Spark makes caches of its computations in the system memory.
  • Data Visualization: A data scientist is expected to be able to visualize the data with the help of Visualization tools such as d3.js, Tableau, ggplot and matplotlib. It enables data scientists to quickly grasp insights from a particular data and enables them to act on the new outcome thus obtained.
  • Unstructured data: A data scientist should be able to work with unstructured data, which is content that is not labelled and organized into database values. Examples of unstructured data include videos, social media posts, audio samples, customer reviews, blog posts etc.

Below are the behavioural traits employers look for in a Data Scientist -

  • Curiosity – Since you will be dealing with massive amounts of data every single day, you need to have curiosity to keep you going. 
  • Clarity – Your concepts should be clear as this is going to be a high-responsibility job. 
  • Creativity – Finding innovative ways to visualize data and interpreting them to make important decisions requires creativity.

There are many benefits to being in the job declared as the ‘Sexiest job of the 21st century’ by Harvard Business review:

  1. High Pay: Owing to high demand and low supply, data scientist jobs are one of the highest paying jobs in the IT industry today. The average salary for a Data Scientist, IT in India is Rs 699,928.
  2. Bonuses: Although it is a part of their pay, data scientists can expect impressive bonuses.
  3. Education: By the time you become a data scientist, you would probably be having either a Masters or a PhD and could receive offers to work as a lecturer or as a researcher for governmental as well as private institutions.
  4. Mobility: Many businesses that collect data are mostly located in developed countries. Getting a job in one would fetch you a hefty salary as well as raise your standard of living.
  5. Network: Your involvement in the tech world through research papers in international journals, tech talks at conferences and many more platforms would help expand your network of data scientists.

Data Scientist Skills and Qualifications

Below is the list of business skills needed to become a data scientist: 

  1. Analytic Problem-Solving – In order to find a solution, it is important to first understand and analyse what the problem is. To do that, a clear perspective and awareness of the right strategies are needed.
  2. Communication Skills – Explaining customer analytics and deep learning insights to companies is one of the key responsibilities of data scientists. You need to have terrific communication skills for this.
  3. Intellectual Curiosity – Delivering results for commercial purpose but imbuing them with creative integrity requires an intellectual curiosity that drives you to deliver the best.
  4. Industry Knowledge – Having a sound knowledge of the industry will give you a good idea of how to approach things in a systematic manner.

Here are some of the ways to brush up your data science skills:

  • Boot camps: Boot camps are the perfect way to brush up your Python basics. Offering both theoretical and practical experience, they usually last anywhere from 4 to 5 days.
  • MOOC courses: These are online courses and include some of the latest trends in the industry.
  • Certifications: These teach you skills with a certification to boost up your CV. Some of the famous data science certifications are:
    • Applied Artificial Intelligence with Deep Learning, IBM Watson IoT Data Science Certificate
    • Cloudera Certified Associate - Data Analyst
    • Cloudera Certified Professional: CCP Data Engineer
  • Projects: Projects help you explore new prospects in your field. Pick projects of your choice, and the more you work on them, the more refined your skills will be.
  • Competitions: Competitions like Kaggle etc. help in building your problem-solving skills.

Today’s world runs on data. Every company – big or small, MNC or a startup – has tremendous use of data produced every day. Associates, Dell, Ericsson, Google, Ibibo India, Ixigo, Expedia, Microsoft, Oracle, Qualcomm, Royal Bank of Scotland, etc. are some of the companies in Gurgaon looking for data scientist. Not just big MNCs but you can also find opportunities in small and mid size companies. Small companies use tools like Google Analytics as they have fewer resources as well as fewer data to work with. Mid-size companies have data but would need someone to apply ML techniques on it to leverage it.

To practise your data science skills, you should work on the following data science problems, categorized according to their difficulty level as compared to your expertise level:

  • Beginner Level
    • Iris Data Set: [4 columns, 50 rows] The Iris data set is said to be the easiest data set to incorporate during your learning of various classification techniques. This is the best data set for beginners to embark on their journey in the field of Data Science.Practice Problem: Predict the class of a flower on the basis of the following particulars.  
    • Loan Prediction Data Set: [13 columns, 615 rows] The Loan Prediction data set provides the learner with a taste of working with the concepts that are applicable in the domain of banking and insurance - the challenges faced, the strategies implemented, the variables that influence the outcomes etc.Practice Problem: Predict if a given loan will be approved by the bank or not.
    • Bigmart Sales Data Set: [12 variables, 8523 rows] Another industry that makes heavy use of analytics in order to optimize business processes is the Retail sector. Operations such as Product Bundling, offer customizations, inventory management etc are efficiently handled with the help of Data Science and Business Analytics.
      Practice Problem: Predict the future sales of this retail store.
  • Intermediate Level
    • Black Friday Data Set: [12 columns, 550,069 rows] The Black Friday Data Set comprises of sales transactions that were captured from a retail store. The Black Friday data set is a regression problem.
      Practice Problem: Predict the total amount of purchase made by this retail store.
    • Human Activity Recognition Data Set: [561 columns, 10,299 rows] The Human Activity Data Set has a collection of 30 human subjects that were collected via recordings by smartphones that were embedded with inertial sensors.
      Practice Problem: Predict the human activity in this category.
    • Text Mining Data Set: [30,438 rows, 21,519 columns] This data set consists of aviation safety reports that describe the problems that were encountered on certain flights.
      Practice Problem: Classify the following documents based on their labels.
  • Advanced Level
    • Urban Sound Classification: [8,732 sound clippings, 10 classes] The Urban Sound Classification data set provides an introduction and an opportunity for implementation of Machine Learning concepts to real world problems.
      Practice Problem: Classify the type of sound that is obtained from this audio.
    • Identify the digits data set: This data set comprises of 7000 images, totalling 31MB, with dimensions of 28X28 each. It helps the developer to study the elements of an image in great detail.
      Practice Problem: Identify the digits present in this image.
    • Vox Celebrity Data Set: The Vox Celebrity Data Set is meant for large scale speaker identification. It is a collection of words spoken by celebrities and extracted from YouTube videos. This data set consists of 100,000 words spoken by 1,251 celebrities from around the world.
      Practice Problem: Find out the celebrity that this particular voice belongs to.

How to Become a Data Scientist in Gurgaon, India

Below are the right steps to becoming a successful data scientist:

  1. Getting started: Choose a programming language in which you are comfortable. We suggest Python or R languages.
  2. Mathematics and statistics: The science in data science is all about dealing with the data (maybe numerical, textual or an image), making patterns and relationships between them.
  3. Data visualization: By visualization, you make data as simple as possible so that the other non-technical teams are able to grasp its contents as well. It is important to learn data visualization in order to communicate better with the end users.
  4. ML and Deep learning: Having deep learning skills to go along with basic ML skills on the CV is a must for every data scientist as it is through deep learning and ML techniques that you will be able to analyze the data given to you.

Here is a list of key skills & steps required to get started:

  • Degree/certificate: Not only will you learn how to apply cutting-edge tools but also get a boost in your career growth by adding something credible to your CV. 
  • Unstructured data: The job of a data scientist boils down to discovering patterns in data that is unstructured and doesn’t fit into a database. This step is complex and needs time and skill.
  • Software and Frameworks: Due to the huge amount of unstructured data, it is essential that you are comfortable in using some of the most popular and useful software and frameworks.
    • Around 43% of scientists use R language. It is commonly used despite having a steep learning curve.
    • Hadoop is the framework used by a majority of data scientists in situations when the amount of data is in excess compared to the memory at hand. It also helps in preventing the loss of data in data science which is sometimes the case in Hadoop.
    • After learning the programming language and framework, it is important that we have in-depth knowledge of databases as well. It is expected from a data scientist that he/she is proficient in SQL queries.
  • Machine learning and Deep Learning: Machine Learning and Deep Learning deal with the application of concepts to the real-world problems. 
  • Data visualization: A data scientist’s job is to make sense of huge amount of data given for analysis and provide it to the business in the form of graphs and charts. Some of the tools used for this purpose include matplotlib, ggplot2 etc.

Around 88% of data scientists hold a Master’s degree and 46% are PhD degree holders. A degree is very important because of the following:

  • Networking – While pursuing the degree, you will get the opportunity to make friends and acquaintances.
  • Structured learning – Following a particular schedule and keeping up with the curriculum is more effective and beneficial than doing things unplanned.
  • Internships – Interning in firms will give you a proper job experience while learning something new.
  • Recognized academic qualifications for your résumé – A degree from a prestigious institution will not only look good but will also give you a head start in the race for the top jobs.

If your total score adds up to more than 6 points, it would be advisable for you to earn a Master’s degree.

  • Have a strong STEM background: 0 point
  • Have a weak STEM background (i.e., biochemistry/biology/ economics or similar degree/diploma): 2 points
  • Have a non-STEM background: 5 points
  • Have less than 1 year of experience with Python: 3 points
  • Have never been part of a job that requires you to code on a regular basis: 3 points
  • Not good at independent learning: 4 points
  • You do not understand that this very scorecard is a regression algorithm: 1 point

Yes, you need knowledge of programming to deal with the following elements: 

  • Data sets: Data is stored in data set – categorised or uncategorised. You need to work with data sets every day to make intelligent use of your data. 
  • Statistics: If a data scientist has knowledge about statistics but has no idea how to implement this knowledge, the knowledge of statistics becomes much less useful in his/her application of data science in his/her field of work.
  • Framework: A data scientist builds systems that an organization can make use of in order to create frameworks to automatically analyse experiments, visualize data as well as manage the data pipeline at a large organization.

Data Scientist Jobs in Gurgaon, India

Here is the logical sequence of steps you should follow to get a job as a Data Scientist.

  • Getting started: Choose a programming language in which you are comfortable. We suggest Python or R language. Understand what data science actually means and the roles and responsibilities of a data scientist.
  • Mathematics: Data science is all about making sense of raw data, finding patterns and relationships between them and finally representing them. Therefore, we have compiled some of the topics which you can pay special attention to:
    • Descriptive statistics
    • Probability
    • Linear algebra
    • Inferential statistics
  • Libraries: Data science process involves various tasks ranging from pre-processing the data given to plotting the structured data and finally to applying ML algorithms as well. Some of the famous libraries are:
    • Scikit-learn
    • SciPy
    • NumPy
    • Pandas
    • ggplot2
    • Matplotlib
  • Data visualization: It’s your job to make sense of the data given to you by finding patterns and making it as simple as possible. The most popular way to visualize data is by creating a graph. There are various libraries that can be used for this task:
    • Matplotlib - Python
    • Ggplot2 - R
  • Data pre-processing: Due to the unstructured form of data, it becomes necessary for data scientists to pre-process this data in order to make it analysis-ready. Pre-processing is done using feature engineering and variable selection. After pre-processing, your data would be in a structured form and ready to be injected into ML tool for analysis.
  • ML and Deep learning: Having deep learning skill to go along with basic ML skills on the CV is a must for every data scientist. For data analysis, deep learning is highly preferred as deep learning algorithms are designed to work when you have to deal with a huge set of data
  • Natural Language processing: Every data scientist should be an expert in NLP as it involves processing of text form of data and its classification as well. 
  • Polishing skills: Competitions like Kaggle etc. present opportunities to exhibit your data science skills. Apart from online competitions, you can keep on experimenting and exploring the field by creating your own projects as well.

Follow these steps to increase your chances of success at landing your dream job:

  • Study: To prepare for an interview, cover all important topics, including-
    • Probability
    • Statistics
    • Statistical models
    • Machine Learning
    • Understanding of neural networks
  • Meetups and conferences: Tech meetups and data science conferences are the best way to start building your network or expanding your professional connections.
  • Competitions: Implement, test and keep polishing your skills by participating in online competitions like Kaggle. 
  • Referral: According to a recent survey, referrals are the primary source of interviews in data science companies. 
  • Interview: If you think you are all equipped for the interviews, then go for it. Learn from the questions that you were not able to answer and study them for the next interview.

A data scientist is an individual who is responsible for discovering patterns and inferencing information from vast amounts of structured as well as unstructured data, in order to meet the business goals and needs. 

Data Scientist Roles & Responsibilities:

  • Fetching data that is relevant to the business from among the huge amount of data that is available in the form of Structured as well as Unstructured Data.
  • Organize and analyze the data that is extracted from the piles of data.
  • Creation of Machine Learning techniques, programs, and tools in order to make sense of the data.
  • Perform statistical analysis with the aim of predicting future outcomes from it.

Gurgaon is the hub of industrialization in India. Big and small companies grace its landscape. Due to high demand and less number of data scientists available, they earn base salaries up to 36% higher than other predictive analytics professionals. The salary of a data scientist depends on 2 things:

  • Type of company
    • Start ups: Highest pay 
    • Public: Medium pay 
    • Governmental & Education sector: Lowest pay 
  • Roles and responsibilities
    • Data scientist: ₹ 699,928/yr
    • Data analyst: more than ₹1,82,000/yr
    • Database Administrator: more than ₹7,00,000/yr

A career path in the field of Data Science can be explained in the following ways:

Business Intelligence Analyst: A Business Intelligence Analyst is an individual who has the job of figuring out the business as well as the market trends.

Data Mining Engineer: A Data Mining Engineer is an individual who has the job of not only examining the data for the needs of the business, but also for a third party.

Data Architect: The role of Data Architect is to work in tandem with system designers, developers and users in order to create blueprints that are used by data management systems.

Data Scientist: The main responsibility of a Data Scientist is to pursue a business case by analysis, development of hypotheses as well as the development of an understanding of data, so as to explore patterns from the given data.

Senior Data Scientist: A Senior Data Scientist is tasked with the anticipation of Business needs in the future and shaping the projects, systems and data analyses of today to suit those business needs in the future.

Below are the professional organizations data scientists can be a part of, regardless of their location:

  • Data Science Association
  • IIA - International Institute for Analytics
  • IMLS - International Machine Learning Society
  • AAi - Advanced Analytics Institute
  • American Statistical Association.
  • NCDM - National Center for Data Mining

Some of the ways to network with data scientists are:

  • Referrals.
  • Data science conference
  • Online platforms like LinkedIn, Fiver.
  • Social gatherings like Meetup 

There are several career options for a data scientist in Gurgaon 2019 – 

  1. Data Scientist
  2. Data Architect
  3. Data Administrator
  4. Data Analyst
  5. Business Analyst
  6. Marketing Analyst
  7. Data/Analytics Manager
  8. Business Intelligence Manager

Here is what employers look forward in Data Scientists:

  • Education: Data scientists have more PhDs than any of the other job titles. So, getting a degree will be beneficial. Getting certified also adds to it.
  • Programming: You should at least be familiar with Python when going for Data Scientists job.
  • Machine Learning: After preparing the data, deep learning is used to analyze the patterns and find a relationship. Having ML skills is a must.
  • Projects: The best approach to learn data science is by practising with real-world projects so that you can build your portfolio.

Data Science with Python Gurgaon, India

Python is a multi-paradigm programming language and the inherent simplicity and readability of Python as a programming language makes it a language that is preferred by data scientists. Another great thing about Python which makes it the language of choice for data scientists is the broad and diverse range of resources that are available at the disposal of a data scientist, should he/she get stuck at a particular point. The Python community is all over the world. It is easy for a developer to get help in resolving his/her problems because the chances are that someone else had been stuck at the same problem in the past and its resolution has already been found.

As data science is a huge field and involves multiple libraries to work together in a smooth way, it is essential that you choose an appropriate programming language.

  • R: One of the most popular despite having a rather steep learning curve.
    • Includes loads of statistical functions and handles matrix operations smoothly.
    • Via ggplot2, R provides us with a great data visualization tool.
  • Python: Though it has fewer packages than R, python is still one of the most sought-after languages in the data science field.
    • Pandas, scikit-learn, and tensorflow provide with most of the libraries needed for data science purposes.
    • Easy to learn and implement.
    • It has a big open-source community as well.
  • SQL: SQL is a structured query language which works upon relational databases.
    • Pretty readable syntax.
    • Efficient at updating, manipulating and querying data in relational databases.
  • Java: Even though it has less number of libraries for data science purposes and java’s verbosity limits its potential, it has many advantages as well:
    • Compatibility: Due to the availability of already coded systems in Java at backend, it is easier to integrate java data science projects to it.
    • It is a high-performance, general purpose, and a compiled language.

  • Scala: Scala has a complex syntax to itself. Still, it is a preferred language in data science domain due to the following advantages:
    • As it runs on JVM, any Scala program can run on Java as well.
    • When combined with Apache Spark, delivers high-performance cluster computing.

Following are the steps to successfully install Python 3 on windows:

  • Download & setup: Go to the download page and setup your python on your windows via GUI installer. While installing, select the checkbox at the bottom asking you to add Python 3.x to PATH, which is your classpath and will allow you to use python’s functionalities from terminal.

You can also install python via Anaconda if you wish. Check if python is installed by running the following command, you will be shown the version installed:

python --version

  • Update and install setuptools and pip: Use below command to install and update 2 of most crucial libraries (3rd party):

python -m pip install -U pip

Note that you can install virtualenv to create isolated python environments and pipenv, which is a python dependency manager.

You can install python 3 from its official website through a .dmg package, but we recommend using Homebrew to install python as well as its dependencies. To install python 3 on Mac OS X, follow:

  • Install xcode: To install brew, you need Apple’s Xcode package, so start with the following command and follow through it:

$ xcode-select --install

  • Install brew: Install Homebrew, a package manager for Apple, using following command:

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Check that it is installed by typing: brew doctor

  • Install python 3: To install the latest version of python, use:

brew install python

To confirm its version, use: python --version

We recommend that you also install virtualenv, which will help you create isolated places to run different projects and may run even on different python versions.

Data Science with Python Course Curriculum

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Learning objectives
Understand the basics of Data Science and gauge the current landscape and opportunities. Get acquainted with various analysis and visualization tools used in data science.


Topics

  • What is Data Science?
  • Data Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools and Technologies 

Learning objectives
The Python module will equip you with a wide range of Python skills. You will learn to:

  • To Install Python Distribution - Anaconda, basic data types, strings, and regular expressions, data structures and loops, and control statements that are used in Python
  • To write user-defined functions in Python
  • About Lambda function and the object-oriented way of writing classes and objects 
  • How to import datasets into Python
  • How to write output into files from Python, manipulate and analyse data using Pandas library
  • Use Python libraries like Matplotlib, Seaborn, and ggplot for data visualization

Topics

  • Python Basics
  • Data Structures in Python 
  • Control and Loop Statements in Python
  • Functions and Classes in Python
  • Working with Data
  • Data Analysis using Pandas
  • Data Visualisation
  • Case Study

Hands-on

  • How to install Python distribution such as Anaconda and other libraries
  • To write python code for defining as well as executing your own functions
  • The object-oriented way of writing classes and objects
  • How to write python code to import dataset into python notebook
  • How to write Python code to implement Data Manipulation, Preparation, and Exploratory Data Analysis in a dataset

Learning objectives
In the Probability and Statistics module you will learn:

  • Basics of data-driven values - mean, median, and mode
  • Distribution of data in terms of variance, standard deviation, interquartile range
  • Basic summaries of data and measures and simple graphical analysis
  • Basics of probability with real-time examples
  • Marginal probability, and its crucial role in data science
  • Bayes’ theorem and how to use it to calculate conditional probability via Hypothesis Testing
  • Alternate and Null hypothesis - Type1 error, Type2 error, Statistical Power, and p-value

Topics

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

Hands-on

  • How to write Python code to formulate Hypothesis
  • How to perform Hypothesis Testing on an existent production plant scenario

Learning objectives
Explore the various approaches to predictive modelling and dive deep into advanced statistics:

  • Analysis of Variance (ANOVA) and its practicality
  • Linear Regression with Ordinary Least Square Estimate to predict a continuous variable
  • Model building, evaluating model parameters, and measuring performance metrics on Test and Validation set
  • How to enhance model performance by means of various steps via processes such as feature engineering, and regularisation
  • Linear Regression through a real-life case study
  • Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis
  • Various techniques to find the optimum number of components or factors using screen plot and one-eigenvalue criterion, in addition to a real-Life case study with PCA and FA.

Topics

  • Analysis of Variance (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 for which you are required to build a regression model to predict the property prices
  • Reducing Dimensionality of a House Attribute Dataset to achieve more insights and better modelling

Learning objectives
Take your advanced statistics and predictive modelling skills to the next level in this advanced module covering:

  • Binomial Logistic Regression for Binomial Classification Problems
  • Evaluation of model parameters
  • Model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value
  • Binomial Logistic Regression with a real-life case Study
  • KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K
  • KNN through a real-life case study
  • Decision Trees - for both regression and classification problem
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
  • Using Decision Tree with real-life Case Study

Topics

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

Hands-on

  • Building a classification model to predict which customer is likely to default a credit card payment next month, based on various customer attributes describing customer characteristics
  • Predicting if a patient is likely to get any chronic kidney disease depending on the health metrics
  • Building a model to predict the Wine Quality using Decision Tree based on the ingredients’ composition

Learning objectives
All you need to know to work with time series data with practical case studies and hands-on exercises. You will:

  • Understand Time Series Data and its components - Level Data, Trend Data, and Seasonal Data
  • Work on a 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 Modelling on Stock Price

Hands-on

  • Writing python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Writing python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Writing Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Use ARIMA to predict the stock prices based on the dataset including features such as symbol, date, close, adjusted closing, and volume of a stock.

Learning objectives
This industry-relevant capstone project under the experienced guidance of an industry expert is the cornerstone of this Data Science with Python course. In this immersive learning mentor-guided live group project, you will go about executing the data science project as you would any business problem in the real-world.


Hands-on

  • Project to be selected by candidates.

FAQs on the Data Science with Python Course

Data Science with Python Training

The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:

  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Data visualization with Python libraries: Matplotlib, Seaborn, and ggplot
  • Distribution of data: variance, standard deviation, interquartile range
  • Calculating conditional probability via Hypothesis Testing
  • Analysis of Variance (ANOVA)
  • Building linear regression models, evaluating model parameters, and measuring performance metrics
  • Using Dimensionality Reduction Technique
  • Building Binomial Logistic Regression models, evaluating model parameters, and measuring performance metrics
  • Building KNN algorithm models to find the optimum value of K
  • Building Decision Tree models for both regression and classification problems
  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot
  • Build data distribution models: variance, standard deviation, interquartile range
  • Calculate conditional probability via Hypothesis Testing
  • Perform analysis of variance (ANOVA)
  • Build linear regression models, evaluate model parameters, and measure performance metrics
  • Use Dimensionality Reduction
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics
  • Perform K-means Clustering and Hierarchical Clustering
  • Build KNN algorithm models to find the optimum value of K
  • Build Decision Tree models for both regression and classification problems
  • Build data visualization models for Time Series data and components
  • Perform exponential smoothing

The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.

Yes, our Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Data Science with Python course is ideal for:

  • Anyone Interested in the field of data science
  • Anyone looking for a more robust, structured Python learning program
  • Anyone looking to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researcher

There are no prerequisites for attending this course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -

Hardware requirements

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors
  • 4 GB RAM
  • 10 GB of free space

Software Requirements

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox

System Requirements

  • 32 or 64-bit Operating System
  • 8 GB of RAM

On adequately completing all aspects of the Data Science with Python course, you will be offered a course completion certificate from KnowledgeHut.

In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.

Data Science with Python Workshop

The Data Science with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.

The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.

Our Date Science course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Data Science with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.

Schedules for our upcoming workshops in Data Science with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us at support@knowledgehut.com and we will be happy to get back to you.

What Learners Are Saying

O
Ong Chu Feng Data Analyst
4
The content was sufficient and the trainer was well-versed in the subject. Not only did he ensure that we understood the logic behind every step, he always used real-life examples to make it easier for us to understand. Moreover, he spent additional time to let us consult him on Data Science-related matters outside the curriculum. He gave us advice and extra study materials to enhance our understanding. Thanks, Knowledgehut!

Attended Data Science with Python Certification workshop in January 2020

A
Amanda H Senior Front-End Developer
5

You can go from nothing to simply get a grip on the everything as you proceed to begin executing immediately. I know this from direct experience! 

Attended Full-Stack Development Bootcamp workshop in July 2021

Z
Zach B Front-End Developer
5

The syllabus and the curriculum gave me all I required and the learn-by-doing approach all through the boot camp was without a doubt a work-like experience! 

Attended Front-End Development Bootcamp workshop in June 2021

N
Nathaniel Sherman Hardware Engineer.
5

The KnowledgeHut course covered all concepts from basic to advanced. My trainer was very knowledgeable and I really liked the way he mapped all concepts to real world situations. The tasks done during the workshops helped me a great deal to add value to my career. I also liked the way the customer support was handled, they helped me throughout the process.

Attended PMP® Certification workshop in April 2020

M
Merralee Heiland Software Developer.
5

KnowledgeHut is a great platform for beginners as well as experienced professionals who want to get into the data science field. Trainers are well experienced and participants are given detailed ideas and concepts.

Attended PMP® Certification workshop in April 2020

L
Lauritz Behan Computer Network Architect.
5

Overall, the training session at KnowledgeHut was a great experience. I learnt many things. I especially appreciate the fact that KnowledgeHut offers so many modes of learning and I was able to choose what suited me best. My trainer covered all the topics with live examples. I'm glad that I invested in this training.

Attended PMP® Certification workshop in May 2020

T
Tilly Grigoletto Solutions Architect.
5

I really enjoyed the training session and am extremely satisfied. All my doubts on the topics were cleared with live examples. KnowledgeHut has got the best trainers in the education industry. Overall the session was a great experience.

Attended Agile and Scrum workshop in February 2020

R
Rafaello Heiland Prinicipal Consultant
5

I am really happy with the trainer because the training session went beyond my expectations. Trainer has got in-depth knowledge and excellent communication skills. This training has actually prepared me for my future projects.

Attended Agile and Scrum workshop in April 2020

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Data Science with Python Certification Course in Gurgaon

Gurgaon has grown to be a leading financial and industrial city of India. Located in the National Capital Region near New Delhi, it is in the state of Haryana. The District got its name from the name of Guru Dronacharya; the village was given as gurudakshina to him by his students; the Pandavas and therefore it came to be known as Guru-gram, which in over time became Gurgaon. Thus the District has been there since the times of Mahabharata. The city's economic growth story began when the leading Indian automobile manufacturer Maruti Suzuki India Limited set up a manufacturing plant in Gurgaon in late 1970s.Today, Gurgaon is a hub for more than 250 Fortune 500 companies. Given its rising popularity across industries, Gurgaon is an ideal choice for aspiring professionals to seek job opportunities in this region. Certification courses such as SAFe Agilist Certification Training, PMI-ACP? Certification Training and others could prove to be beneficial. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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