Data Science with Python Training in Noida, 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
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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

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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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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 this 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

1

Python Distribution

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

2

User-defined functions in Python

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

3

Datasets and manipulation

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

4

Probability and Statistics

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

5

Advanced Statistics

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

6

Predictive Modelling

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

7

Time Series Forecasting

Time Series data, its components and tools.

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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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Data Science with Python

What is Data Science

Harvard Business Review called Data Scientist the sexiest job of the 21st Century in 2012. Data is everywhere around us and Data-driven decision making is the need of the hour. From making effective business decisions to classifying target audiences, data science offers great value to businesses. Noida bustles with some of the best companies to work for, including Paytm, Cadence Design Systems, Adobe, Ericsson, etc. All these companies are looking for expert data scientists to help them take informed decisions based on their findings. 

Below are the top technical skills required to become a data scientist: 

  1. Python Coding: Python is one of the most popular 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. 
  2. R Programming: Knowledge of R programming and excellent analytical tools is usually an advantage for data scientists in order to make any data science problem easier to solve.
  3. Hadoop Platform: Though not a requirement for data science, Hadoop Platform is heavily preferred in projects. It is listed as the leading skill requirement for a data science engineer on job portals.
  4. SQL database and coding: SQL is a language that is specifically designed to help data scientists access, communicate as well as work on data. MySQL also possesses concise commands that save time and decrease the level of technical skills required to perform operations.
  5. 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. Topics like Neural Learning and Decision Trees are going to be integral in this field. 
  6. Apache Spark: One of the most popular data sharing technologies worldwide, Apache Spark is a big data computation, not unlike Hadoop. The only difference between Apache Spark and Hadoop 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.
  7. Data Visualization: A data scientist is expected to be able to visualize the data with the help of Visualization tools. This will help convert data into a format that is easy to understand and comprehend. 
  8. Unstructured data: It is important for a data scientist to be able to work with unstructured data, which is content that is not labelled and organized into database values.

Below are the behavioural traits of a successful Data Scientist -

  • Curiosity – As you will be dealing with massive amounts of data every single day, you should have an undying hunger for knowledge to keep you going. 
  • Clarity – Whether you are cleaning up data or writing code, you should know what you are doing and why you're doing it. 
  • Creativity - Creativity in data science allows you to figure out what's missing and what needs to be included in order to get results. 
  • Scepticism – Data scientists need scepticism to keep their creativity in check, so that they do not get carried away and lose focus.

 Here are the 5 proven benefits of being a Data Scientist in Noida:

  1. High Pay: Due 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 is INR 6,65,636 per year in Noida, Uttar Pradesh.
  2. Good bonuses: Data scientists can also expect impressive bonuses and perks in their job.
  3. Education: By the time you become a data scientist, you would probably be having either a Masters or a PhD due to the demand for knowledge in this field. You might even 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 – You need a clear perspective and the know-how of the field you are in. Because in order to find a solution, it is important to first understand and analyse what the problem is.
  2. Communication Skills – Communicating customer analytics or deep business to companies is one of the key responsibilities of data scientists.
  3. Intellectual Curiosity – You must possess the desire to ask questions in order to produce value to the organization.
  4. Industry Knowledge – This is perhaps one of the most important skills. Having a sound knowledge of the data science industry will give you a better idea of what needs attention.

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

  • Boot camps: Boot camps ensure you thoroughly master and understand Data Science concepts in a mere four-five days. 
  • MOOC courses:  MOOCs are the online courses that help learners polish their implementation skills in the form of assignments.
  • Certifications: You can demonstrate your knowledge and skills through certifications that are recognised in the industry. Some renowned data science certifications:
    • Applied AI 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 already answered questions in your own way. Experiment around and polish your skills at your desired pace.
  • Competitions: Competitions improve your problem-solving skills in real-world situations. One such competition is Kaggle.  

We live in a world of data. Noida was ranked as the Best City in Uttar Pradesh and is home to several leading companies like HCL Technologies, Monotype, Tata Consultancy Services, Tech Mahindra, Infogain, Paytm, etc. Whether it’s a startup or an MNC, Data is valuable to all these companies as it tells them about their audience’s interests, allowing them to improve their customers’ experiences. So, all these companies are looking for skilled data scientists to do the job.

We’ve compiled a list of data sets you can practice on, categorized on the basis of difficulty:

  • Beginner Level
    • Iris Data Set: [4 columns,50 rows] This is the best data set for beginner to embark on their journey in the field of Data Science. The Iris data set is said to be the easiest data set to incorporate during your learning of various classification techniques. Practice Problem: The problem is using these parameters to predict the class of the flowers. 
    • Loan Prediction Data Set: [13 columns, 615 rows] The learner will have to work with concepts applicable in banking and insurance including the variables that affect the outcome, the implemented strategies and the challenges faced.Practice Problem: The problem is to predict if the loan will be approved or not. 
    • Bigmart Sales Data Set: [12 variables, 8523 rows] The Retail sector uses this data set for operations like Product Bundling, offering customizations and inventory management.Practice Problem: The problem is predicting the sales of the 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. It helps gain an understanding of the day to day shopping experiences of millions of customers. Practice Problem: The problem is predicting the total amount of purchase.
    • Human Activity Recognition Data Set: [561 columns, 10,299 rows] The set has a collection of 30 human subjects collected via recordings.
      Practice Problem: The problem is the prediction of the category of human activity.
    • Text Mining Data Set: [30,438 rows and 21,519 columns] This data set consists of aviation safety reports describing the problems encountered on certain flights.
      Practice Problem: The problem is the classification of documents based on their labels. 
  • Advanced Level:
    • Urban Sound Classification: [8732 sound clippings, 10 classes] The Urban Sound Classification data set deals with the implementation of Machine Learning concepts to real world problems. It also includes the concepts of audio processing.
      Practice Problem: The problem is the classification of the sound obtained from specific audio. 
    • Identify the digits data set: This data set comprises of 7000 images of 31MB and 28X28 dimensions. This data set helps you in studying, analyzing, and recognizing elements present in a particular image.
      Practice Problem: The problem is identifying the digits present in an image. 
    • Vox Celebrity Data Set: This data set consists of 100,000 words spoken by 1,251 celebrities from around the world. The Vox Celebrity Data Set is meant for large scale speaker identification.
      Practice Problem: The problem is the identification of the voice of a celebrity.

How Can I Become A Data Scientist in Noida, India

Below are the steps to becoming a successful data scientist in Noida:

  1. Getting started: First things first, choose a language you are comfortable with. Most people go for Python or R Language.
  2. Mathematics and statistics: Both subjects form the backbone of Data Science. You need to have a good understanding of basic algebra and statistics.
  3. Data visualization: It is important to learn data visualization in order to communicate better with the end users in terms of explaining the data in a coherent and informed way.
  4. ML and Deep learning: Having deep learning skills to go along with basic ML skills is a must as it is through deep learning and ML techniques that you will be able to analyse the data given to you.

We have compiled a list of needed key skills & steps required to get started in this direction:

  1. Degree/certificate: Be it an online or offline classroom course, it is important to start with a basic course that covers the fundamentals. More importantly, it boosts up your resume. 
  2. Unstructured data: The job of a data scientist boils down to discovering patterns in data. Usually, the data is unstructured and doesn’t fit into a database. Your job is to understand and manipulate this unstructured data.
  3. Software and Frameworks: It is essential that you are comfortable in using some of the most popular and useful software and frameworks to go along with an equally important programming language - preferably R.
  4. Machine learning and Deep Learning: Data scientists must have knowledge of ML and Deep learning in order to make their mark in the Data Science world.
  5. 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.

A degree is helpful because of the following – 

  • Networking – While pursuing the degree, you will get the opportunity to make friends and acquaintances from the same industry.
  • Structured learning – Following a particular schedule and keeping up with the curriculum is more effective and beneficial than doing things unplanned.
  • Internships – Internships are an excellent opportunity to gather hands-on training, experience a working environment and make acquaintances at the same time.
  • 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 score is more than 6 points, you should get a Master’s degree:

  • A strong STEM (Science/Technology/Engineering/Management) background: 0 point
  • A weak STEM background (biochemistry/biology/economics or another similar degree/diploma): 2 points
  • A non-STEM background: 5 points
  • Less than 1 year of experience in Python: 3 points
  • No experience of a job that requires regular coding: 3 points
  • Independent learning is not your cup of tea: 4 points
  • Cannot understand that this scorecard is a regression algorithm: 1 point

Yes, programming knowledge is a must in the field of Data Science because of the following reasons:

  • Data sets: Programming knowledge helps you make sense out of structured and unstructured data sets.
  • 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: The programming ability of a data scientist also enables him/her to perform data science in a proper and efficient manner. This also enables a data scientist to build systems that an organization can make use of in order to create frameworks to automatically analyse data.

Data Scientist Jobs in Noida, India

If you want to get a job in the field of Data Science, you need to follow this path:

  • Getting started: Understand what data science actually means and the roles and responsibilities of a data scientist. Then choose a language you are familiar with.
  • Mathematics: Data science is all about making sense of raw data, finding patterns and relationships between them and finally representing them, which is why it is crucial that you have a sound grasp of the subject.
  • 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 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. After pre-processing, our data would be in a structured form and ready to be injected into ML tool for analysis.
  • 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. 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: Data Scientists need to have the required knowledge of processing the text form of data and further classifying it. 
  • Polishing skills: Competitions provide an opportunity for you to learn and exhibit your skills. You can also explore the field by experimenting and creating your own projects. 

Follow the below steps to increase your chances of success:

  • 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 expand your professional connections.
  • Competitions: Competitions are a great way to exhibit your skills to potential employers. 
  • Referral: According to a recent survey, referrals are the primary source of interviews in data science companies. Make sure your profiles on all the job portals are up to date.
  • 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.

The data generated every day is a gold mine of patterns and ideas that could prove to be very helpful for making key business decisions. It is the responsibility of a data scientist to extract the relevant information and make sense of it.

Data Scientist Roles & Responsibilities:

  • Fetching data that is relevant to the business from the huge amount of data provided to them.
  • Organize and analyze the data.
  • Creation of Machine Learning techniques, programs, and tools in order to make sense of the data.
  • Perform statistical analysis on data sets with the aim of predicting future outcomes or taking informed decisions.

The average salary for a Data Scientist is ₹ 6,65,636 per year in Noida, Uttar Pradesh.

A career path in the field of Data Science in Noida 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 examining the data for the needs of the business. He also needs to create sophisticated algorithms that further aid in the analysis of data.

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.

Noida is fast becoming an employment sector where Data Science can flourish. Below are the top professional organizations for data scientists – 

Referrals are the most effective way to get hired. Some of the other ways to network with data scientists are:

  • Data science conferences – Held routinely and immensely helpful.
  • Online platforms – Not just job portals but also forums for communities are a great help.
  • Social gatherings – like Meetup 

There are several career options for a data scientist in Noida today – 

  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

Companies generally prefer data scientists to have mastery over some software and tools. They generally look for:

  • 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: Programming is vital in data science and Python is one of the most prominent languages of programming. So, it is important to learn Python Basics before you start learning any data science libraries.
  • 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 Noida, India

The simplicity of Python makes it popular among data scientists. It is a structured and object-oriented programming language that contains several libraries and packages that are useful for the purposes of Data Science. The Python community is another big advantage. There are millions of developers working on the same problems with the same programming language every day. They form forums, communities and clubs to interact with each other and help solve problems.

Below are the most popular programming languages used in the Data Science field apart from Python:

  • R: It has a steep learning curve, but it does offer various advantages:
    • The big open-source community directly facilitates great open source packages.
    • Includes loads of statistical functions and handles matrix operations smoothly.
    • Via ggplot2, R provides us with a great data visualization tool.
  • SQL: SQL is a structured query language which works upon relational databases. It has following advantages:
    • Pretty readable syntax.
    • Useful in updating and manipulating relational databases.
  • Java: Even though it has less number of libraries and its verbosity is limited, it has many advantages as well, such as:
    • Compatibility. Due to already systems coded 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: It is popular in data science field even though it has a complex syntax because of the following reasons:
    • As it runs on JVM, any Scala program can run on Java as well.
    • Delivers high-performance cluster computing when paired with Apache Spark.

Following are the steps to install Python 3 on windows:

  • Download and setup: Visit the download page to setup Python on Windows. 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 the terminal.

  • You can use Anaconda to do the same as well. 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

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

To install python 3 on Mac OS X, follow the below steps:

  • Install xcode: You will 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)"

Confirm the same 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

Note: It’s advisable to 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

Download Curriculum
1.

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 
2.

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
3.

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
4.

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
5.

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
6.

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.
7.

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.

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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

B
Ben Johnson Developer
5

The Backend boot camp is a great, beginner-friendly program! I started from zero knowledge and learnt everything through the learn-by-doing method. 

Attended Front-End Development Bootcamp workshop in July 2021

E
Emma Smith Back End Engineer
5

KnowledgrHut’s Back-End Developer Bootcamp helped me acquire all the skills I require. The learn-by-doing method helped me gain work-like experience and helped me work on various projects. 

Attended Back-End Development Bootcamp workshop in May 2021

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

Y
Yancey Rosenkrantz Senior Network System Administrator
5

The customer support was very interactive. The trainer took a very practical oriented session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

Attended Agile and Scrum workshop in April 2020

M
Marta Fitts Network Engineer
5

The workshop was practical with lots of hands on examples which has given me the confidence to do better in my job. I learned many things in that session with live examples. The study materials are relevant and easy to understand and have been a really good support. I also liked the way the customer support team addressed every issue.

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

E
Ellsworth Bock Senior System Architect
5

It is always great to talk about Knowledgehut. I liked the way they supported me until I got certified. I would like to extend my appreciation for the support given throughout the training. My trainer was very knowledgeable and I liked the way of teaching. My special thanks to the trainer for his dedication and patience.

Attended Certified ScrumMaster (CSM)® workshop in February 2020

View all

Data Science with Python Certification Course in Noida

An acronym of New Okhla Industrial Development Authority, Noida is a city that was established as part of a planned urbanization thrust in the 1970s.One of the most well planned cities in India, this city has the highest per capita income in the national capital region. This makes it a highly sought-after hot spot for realty, IT and IT services and BPOs. The infrastructural facilities in Noida are state-of-the-art and on par with global standards, and this city may soon surpass Bangalore as the acknowledged software capital of the country. Many top multinationals outsourcing IT services are located here- including Sapient, Headstrong, TCS, Fujitsu, Adobe among others. Due to the multifold advantages it offers, the city offers great scope for professionals in all areas of software technology such as Big Data and Hadoop 2.0 Developer, CEH and so on. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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