Data Science Course with Python in Hyderabad, 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
  • 70 + 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

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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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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 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.

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 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.

Data Science with Python

What is Data Science?

With more than half the population of the world using the internet and social media platforms it has become imperative to store, organize and analyze the billions of data that is being generated every single second of every day. This has led to the need for Data Scientists who can simplify and isolate data that can be used for optimizing businesses by understanding the choices made by consumers.

Hyderabad is one of the major cities of India. It has many leading companies such as Microsoft, IBM, Amazon, Franklin Templeton, INVECAS, Thrymr Software, Woodcutter, PayZello,etc. operating in the city. These enterprises are in search for skilled data scientists to add to their workforce.

 Other major reasons for Data Science becoming a popular career path are:

  • Organizations are relying on data-driven decision making.
  • Since the need for well qualified Data scientists is disproportionately going up in relation to the number of professional Data scientists out there, the tech companies are paying high salaries to the qualified professionals in this field.
  • As the rate of data generation is increasing the need to analyze those data at an equally high rate is demanded. Data scientists can help companies make crucial decisions by providing their findings from segregating raw data.

Hyderabad is home to many renowned technical universities such as IIIT Hyderabad, Havisha Institute, Indian School of Business, Imarticus Learning, University of Hyderabad, Data Analytics Park, etc.which offer courses in the field of data science. The essential skills needed to become a Data Scientist are as follows:

  • Python Coding
  • R Programming
  • Hadoop Platform
  • SQL database and coding
  • Machine Learning and Artificial Intelligence
  • Apache Spark
  • Data Visualization
  • Unstructured data
  1. Python Coding: Python is one of the simplest and most popular coding systems used by Data scientists. It is a versatile and easy to use programming tool that takes various formats of data and processes them. Python also helps in creating datasets and allows data scientists to perform operations on those datasets.
  1. R programming: The knowledge of at least one analytical tool is useful in taking forward your data science journey and becoming an expert. R programming is one such tool, knowledge of which will make any data science problem easier to solve.
  1. Hadoop Platform: This is an open source framework that processes and analyzes huge volumes of data. It is provided by Apache and is a major requirement for most data science jobs even though there are better alternatives available for it.
  1. SQL database and coding: SQL is the language in which data is created. Thus data scientists need to know this language in order to analyze, communicate as well as work on data and understand the structure and formation of a database. MySQL also possess concise commands to make operations on a database easier, while saving time and decreasing the number of technical skills needed by a data scientist.
  1. Machine Learning and Artificial Intelligence: It is mandatory to have a proficiency in the subject of Machine Learning and Artificial Intelligence for professionals who want to pursue a career in data science. Some of the major concepts and knowledge of ML and AI that are necessary to learn are:
  • Reinforcement Learning
  • Neural Network
  • Adversarial learning
  • Decision trees
  • Machine Learning algorithms
  • Logistic regression etc.
  1. Apache Spark: Apache Spark is a developed version of the Hadoop Platform. It is one of the most popular data computation and sharing platforms with the only difference from Hadoop being that Apache Spark is faster than Hadoop. While Hadoop reads and writes to the disk, Apache Spark creates caches of its computation in the system memory. Apache Spark is the effective way of running data science algorithms faster. It helps in the faster dissemination of processed data while making handling of unstructured data set easier. It also diminishes the loss of data and is a faster alternative making it easier for data scientists to carry out projects.
  1. Data Visualization: Visualization tools like d3.js, ggplot, Tableau and matplotlib helps data scientists visualize data. Complex data obtained from performing processes on data sets needs to be transformed into easy formats that can be comprehended by data scientists. Data visualization enables organizations to work directly with the obtained data. The knowledge and outcome obtained from a particular data can be directly used on new data.

  1. Unstructured data: These are the content that are not labelled and organized into database values like videos, social media posts, audio samples, customer reviews, blog posts etc. Data scientists need to work with such unstructured data.

5 behavioral traits are needed in order to become a successful data scientist. 

  1. Curiosity: To work as a data scientist one needs to deal with a massive amount of data on a daily basis. For that one needs to have an insatiable hunger for knowledge to keep going.
  2. Clarity: Having a thorough idea of what is going on around you and ‘why’ and ‘what’ you need to do is essential. Whether cleaning data or writing code, one should have clarity on what needs to be done and why.
  3. Creativity: Creativity is absolute for any form of work, whether it is visualizing data, developing new tools or new modeling features. Being aware of what is missing and what needs to be done when faced with a problem is necessary to get the right results.
  4. Skepticism: One of the perils of creativity and being a successful data scientist is getting carried away by your own creativity. Skepticism keeps a data scientist in check and rationalizes the creative mind to find the right solution and not get attached to their own invention.

Living in Hyderabad is highly beneficial for a data scientist as it is home to some of the elite universities such as IIIT Hyderabad, Indian School of Business, Imarticus Learning, University of Hyderabad, Data Analytics Park, etc. and companies like Microsoft, IBM, Amazon, Franklin Templeton, INVECAS, Thrymr Software, Woodcutter, PayZello, etc.

Moreover, when more than half of the world’s population is using something you are an expert in, you will definitely get to enjoy its benefits.

  1. Highest paying job: It is said that the greater the peril better the reward. Qualifying as a certified data scientist needs a lot of training and hard work, thus the pay has to be proportionate to the work put into it. There is a high demand for data scientists but a limited number of trained professionals out there. Hence, data scientists earn one of the highest salaries in the IT sector today. Today, the average salary for a data scientist in Hyderabad is ₹8,55,143/yr.
  1. Great bonuses: Apart from the salary, data scientists get huge bonuses including equity shares and signing perks.
  1. Education: Becoming a data scientist requires a lot of knowledge. Thus by the time you become an expert you will probably have a Master’s or a PhD which will help you to receive offers as a lecturer or researcher at governmental as well as private institutions.
  1. Mobility: Some of the leading businesses that collect data are situated in developed countries. Getting a job at one of these organizations will lead to earning a better salary and also allow you to even migrate to a country with a better standard of living.

  1. Networking: Being involved in the tech world by publishing research papers in international journals and attending conferences will expand your interaction with people in the industry. You can get referrals from such networks.

Qualifications and Skill Sets of Data Scientists

Below are the top business skills required to become a successful data scientist- 

  1. Analytical problem solving: To find a solution, one needs to have an analytical mind to understand the problem. In order to do that one needs to be aware of all the strategies and have a clear perspective to reach the right solution.
  2. Communication Skills: Collecting data and analyzing it is not the only responsibility of a data scientist. Unless you can communicate the customer analytics or  business needs to stakeholders, your job is only half done.
  3. Intellectual Curiosity: Having a thirst for knowledge and a constant need to ask ‘why’ leads a data scientist to have the right solutions. Unless you are driven by your work and are constantly curious you will not be of great value to a commercial enterprise.
  4. Industry knowledge: This is of great value if you want to be ahead of your competitors. Being up to date with the   happenings in the industry will help you understand what needs your attention and what does not.

While you may become an expert in Data science, it is always preferred that you are up to date with the new developments in data science. For that you need to attend:

  • Bootcamps: Bootcamps are the best way to improve your Python programming skills. Bootcamps are held for 4 to 5 days, offering both theoretical knowledge as well as hands on experience.
  • MOOC courses: These are virtual courses and provide excellent knowledge of latest trends in the industry. These courses are taught by experts helping you refine your implementation skills through assignments.
  • Certification: Certification is an important way of increasing your skill set while also improving your CV. Some of the best data science certification courses are:
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
    • Cloudera Certified Associate - Data Analyst
    • Cloudera Certified Professional: CCP Data Engineer
  • Projects: Projects are a great way to work on new solutions to already worked out problems depending on the restrictions of the projects. The more you work on projects, the better will be your analytical and problem solving skills.

  • Competitions: Attending competitions like Analytics Vidya or Kaggle, etc, improves your problem solving skills while giving you an idea of where you stand in relation to your peers. It also helps to optimize your rational analytic skills staying within the given restraints.

Anything that gives insight to customer preferences is data. From your hospital prescription, stock investments, browsing history, or favorite color, everything is data and can be used by companies to make ideal products, improving customer experience. Thus the kind of data scientist job you are offered says a lot about the kind of company you are being hired for.

  • Companies using Google Analytics are usually small ones for they have less data and resources to work with.
  • Mid size companies have considerable amount of data which needs ML techniques to leverage them.
  • Big companies always have data scientists so when they are hiring new people they would expect specialization, like, data visualization, ML experts and so on.

The best way to master any technique is through practice and to master data science the best approach would be to work through problems while solving data science algorithms. There are few data science problems which can be worked on to increase your skills in data science. They are categorized below according to their difficulty level:

Beginner Level

  • Iris Data Set: Iris Data Set is the most engaging, adaptable, dynamic and comprehensive data set that you can begin your data science journey with. It has only 4 columns and 50 rows of data set that teaches you the various types of classification techniques. It is easy to understand and provide interesting insights into the more complicated aspects of data science.
  • Loan Prediction Data Set: The domain of banking uses data analytics and data science methodologies as much as any other industry. The Loan Prediction Data Set introduces the concepts of data science that are applicable to banking and insurance. It has 13 columns and 615 rows of classification problem data set. The set includes the methods implemented in the detection of common variables that could provide insight into customer behavior and so on.Practice Problem: Predict the probability of a bank to approve a given loan.
  • Bigmart Sales Data Set: The retail store is one of the best places to study data collection, categorization and dissemination between retailers and customers. The use of data science is bound to be seen in this sector. Product placement, offer customization or inventory management are few of the things that is performed by using data science. The data set consists of 12 variables and 8523 rows used to determine Regression problems.
    Practice Problem: Sales prediction of a retail store.

Intermediate level

  • Black Friday Data Set: This is another retail store data set where the sales trajectory of a retail store is analyzed. The data set explores and expands on feature engineering skills and understand daily transactions of hundreds of customers. This data set is a regression problem consisting of 550069 rows and 12 columns.
    Practice Problem: Predicting purchase amount.
  • Trip History Data Set: This data set comes from a bike sharing service in the United States. Your pro data munging skills are tested in this data set. The quarter-wise data from 2010 is provided, with each file having 7 columns. This is a classification problem.
    Practice Problem: The class of users is required to be predicted.
  • Million Song Data Set: This is an interesting data set and can appeal to most people. It is based on the entertainment industry. This is a regression problem with 515345 observations and 90 variables which is not the complete database. This data set is a subset of the original data set of a million songs.
    Practice Problem: Predicting the year of release of a song.

Advance Level

  • ImageNet Data Set: This data set is a unique one for it includes lots of different variables like object detection, localization, classification and screen parsing. There are a number of images are easily available and you can create your project around any of them. As recorded till now, the search engine has over 15 million images together creating around 140gb of data.
    Practice Problem: The problem depends on the image you have downloaded.
  • Chicago Crime Data Set: Since data science is all about working with data all organizations expect data scientists to work with large amount of data. They no longer want to work with sample data when there are methods of computation of complete data sets. This data set is an important introduction to handling of large data sets using your local devices. While the problem is easy, the secret lies in your data management skills. This data set consists of 6 million observations making it a multi-classification problem.
    Practice Problem: Prediction of the type of crime.

  • Age Detection of Indian Actors Data Sets: This is another interesting data set. In this data set your task is to identify the age of actors from the thousands of images of actors. The images are selected manually from multiple videos thus providing variations in quality, size, resolution, expression, illumination, occlusion and make up. There are two types of sets- training set consisting 19906 images and test set consisting 6636 images.
    Practice Problem: Predicting the age of actors.

How to Become a Data Scientist in Hyderabad, India

The following points will guide you to become a successful data scientist,

  1. Preparation: One of the first steps towards becoming a data scientist is to learn a programming language. Either Python or R programming are the most common ones that one can master.
  2. Mathematics and statistics: It is commonsensical that data science will deal with data, which can be in any form-numerical, textual or an image- which need to be compared and categorized. Having basic skills of algebra and statistics will make it easier to grasp the concepts of data science.
  3. Data visualization: The work of a data scientist is not just to understand data themselves but make it simple and coherent enough that non-experts can understand it perfectly. Visualization of data becomes an important aspect of data science as it is the end user who needs to understand the data generated more than the scientific aspect of gene analysis.
  4. Deep Learning and ML: Having knowledge of deep learning and ML are a must for any data scientist. It is through the skills of deep learning and ML that data scientists analyze the data provided.

Some of the most successful companies in the world rely on data science for their business growth. Google, Amazon, Facebook have offices in Hyderabad and also have the highest rate of employing data scientists. In such a scenario what should you do to get ahead of your peers? Below, listed, are the skill sets and steps you should take,

  1. Get a degree: Data scientists are considered to mostly consist of Master’s or PhD degree holders. Hence, it is important to start preparing, reading and practicing as early as you can. You could get into numerous programs online or offline, or get yourself a degree on basics of mathematics and algebra. Having a degree in computer science or statistics is considered valuable. Thus a certificate or degree course would be your first step.
  2. Ability to handle a large quantity of data: Handling unstructured data is essentially the job of a data scientist. How to categorize the infinite number of data getting stored and made cohesive is the most important responsibility as it entails a lot of complexity. Fitting the unstructured data into a database needs more than technical skill, thus becomes a trickier job. Working on data sets and projects can improve ones eye for useful data.
  3. Software and techniques to master: The softwares like Python, R and Hadoop are important tools to stay accustomed with. More than 53% data scientists are fluent in both R and Python programming. Being accustomed to using these will kick-start your data science career:
    • R is a versatile programming language. It is easy to use and highly popular with most companies. Python is slowly catching up with R in terms of popularity and should also be learnt.
    • Hadoop is a useful software especially when there are more data than there is storage. Hadoop quickly transfers the data to different points in the machine. Apache Spark is sometimes favored over Hadoop by many companies now. It is similar in its computation work to Hadoop but is faster and more reliant method of storing data.
    • Understanding and collection are the preliminaries to interpreting data and classification of database. SQL queries are important to be learned for this reason.

As mentioned above, most data scientists are Master’s or PhD degree holders. Around 75% are PhD scholars with some background in computer science, mathematics or social sciences. There are perks of having a quantitative education base-

Networking: Interacting with your peer group will help improve clarity and you will find networking opportunities. Having acquaintance in the industry always gives people an edge.

Structured learning: Having a schedule for your curriculum will not only provide a holistic idea about the discipline, it will help in maintaining a schedule and being more productive than in an unplanned situation.

Internships: Getting hands on experience by doing internships can be very helpful and provide you with an idea about the workload you will be expected to take up.

Appropriate academic degrees and qualification: Along with having a degree from a prestigious university, it is also important that you have hands-on experience. In the United States the most common sector which hires data scientists are manufacturing, FMCG, Utility, Consultancy and so on. In contrast data scientists in India are mostly hired in IT and Tech sector or healthcare and financial industries. Thus it is important to have a clear goal at the earliest about which sector one can work in or one wants to work in, so that he/she can pursue the right degree.

Hyderabad is home to IIIT Hyderabad, Havisha Institute, Indian School of Business, Imarticus Learning, University of Hyderabad, Data Analytics Park, etc. These are some of the best universities in India which offer advanced courses in the field of data science. The necessity of a Master’s degree depends on the following points mentioned below. Score yourself according to the factors mentioned, if you score more than 6 points it is advisable that you get done with a master’s degree.

  • You have a strong STEM (Science/Technology/Engineering/Management) background: 0 points.
  • You have a weak STEM background (Biochemistry/Biology/Economics or other such degrees): 2 points.
  • You come from a non-STEM background: 5 points
  • You have less than 1 year experience of working with Python programming: 3 points
  • You have never had a job which required you to code on a regular basis: 3 points
  • You feel you are not good at independent learning: 4points
  • You do not understand when it is said that this scorecard is a regression algorithm: 1 point.

Programming is at the heart of data science and is an absolute must for anyone to learn in order to become a Data Scientist. The other reasons are as follows:

Data sets: A job of a data scientist revolves around analysis of large number of data sets. Knowledge of programming is required to help you analyze those data sets.

Statistics: The ability to program goes hand in hand with your ability to use statistics. As you start working on programming, a lot of statistical techniques will be identified which in turn will make it easier for you to write codes. Without the knowledge of implementation of statistics in data science, statistics will prove to be useless.

Framework: Having programming ability improves an individual's efficiency and ability to structure the  data. It is important that data scientists create frameworks for analyzing data so that visualization, interpretation and data pipeline is constructed which will allow selected individuals to access the data at any time. Making the work space efficient is the ultimate responsibility of a data scientist.

Data Scientist Salary in Hyderabad, India

The Data Scientist in Hyderabad will earn an income of about Rs. 6,13,889.

The average salary of a Data Scientist in Hyderabad is Rs. 6,13,889 as compared to Rs. 8,19,815 in Pune.

The average salary of a data scientist in Hyderabad is Rs. 6,13,889 as compared to Rs. 6,15,496 in Bangalore.

The annual earnings of a Data Scientist in Hyderabad is Rs. 6,13,889 as compared to Rs. 8,19,815 in Chennai.

Hyderabad is home to several organizations that have just started to dabble in the field of Data Science. They are still new and are searching for data scientists to help them convert their raw data into useful business insights. This makes the demand for Data scientists in the city quite high.

A Data scientist employed in Hyderabad enjoys several benefits. Hyderabad is home to several large as well as medium and small size organizations that are keen on using the power of data to make important business decisions. A data scientist will also get to meet several like minded individuals here through conferences and meetups, as Hyderabad is a hotspot for such events.

Being a data scientist in Hyderabad offers tremendous perks and advantages. The city is filled with startups and established organizations that are looking for data scientists. The city is also a hotspot for several data science conferences, summits, and meetups that allow data scientists to connect and network. They will even get the opportunity to connect with top-level executives in Hyderabad. Also, there are several training institutes that offer certification courses in the field of data science that will help them excel in their field. It's a great city for any Data Scientist to work in their field of interest and enjoy lucrative positions.

The top companies with openings for Data Scientists in Hyderabad are UnitedHealth group, Chiselon Technologies, Accenture, SoCtronics, Julien 

Innovations India Pvt Ltd, ZF, Vitreous Health, IBM, Microsoft, JDA Software, etc.

Data Science Conferences in Hyderabad, India

S.NoConference nameDateVenue
1.International Conference on Internet of Things, Big Data Analytics and Information Technology (ICITBDIT - 2019)25 May, 2019Hampshire Plaza Hotel, 679 & 80, Lakdikapul, Hyderabad-500004, India
2.International Conference on Big Data, IoT, Cyber Security and Information Technology (ICBDICSIT)
2 June, 2019
Hampshire Plaza Hotel, 679 & 80, Lakdikapul, Hyderabad-500004, India
3.International Conference on Artificial Intelligence & Cognitive Computing
30-31 Aug, 2019
MLR Institute of Technology, Near Gandimysamma Police Station Road, Dundigal, Quthbullapur, Hyderabad, Telangana 500055
4.Practical Data Science By Industry Experts
18 May, 2019
Metasoft Solutions / msmeCloud Pvt. Ltd. Plot No: 12, 2nd Floor,Silicon Valley, Hi-tech City,Madhapur, Hyderabad, Telangana 500081
5.Alexandria | 2 Day Course in NGS Data Analysis & Crispr
18-19 July, 2019
The Grand Plaza Smouha Hotel, Hyderabad
6.Interactive Data Science Demo
11 May, 2019
Kelly Technologies, Flat no. 212, 2nd floor, Annapurna block, Aditya Enclave, Amerpeet, Kumar basti, Hyderabad, Telangana, 500016
7.Data Science and Machine Learning Demystified
11 May, 2019
International School of Engineering (INSOFE), Vamsiram Builders, Hyderabad, India
8.Data Science Course Free Demo By Kelly Technologies
May 12, 2019
Kelly Technologies, Flat no : 212, 2nd floor, Annapurna Block, Aditya Enclave, Ameerpet, Hyderabad-16.
9.Best Azure Data Analytics Training Institute in Hyderabad-AcuteLearn Technologies
9 May, 2019
Acutelearn Technologies, Vip Hills, Jaihind Enclave, Madhapur, Hyderabad, India

1. International Conference on Internet of Things, Big Data Analytics and Information Technology (ICITBDIT - 2019), Hyderabad

  • About the conference: The conference calls for paper submission of original research work and provides a platform for delegates to share their innovative ideas and insights on the technologies like IoT, Big Data and analytics, paving a pathway for future development.
  • Event Date: 25 May, 2019
  • Venue: Hampshire Plaza Hotel, 679 & 80, Lakdikapul, Hyderabad-500004, India
  • Days of Program: 1
  • Purpose: The purpose of the conference is to provide a platform for discussion and sharing ideas and experiences with the other delegates and peers from around the world.
  • Registration cost: 
  • Authors (Academician/Practitioner): 
    • Registration Fee For Non Indian Nationals: 300 USD
    • Registration Fee For Indian: 7000 INR
  • Authors (Student Masters/PhD): 
    • Registration Fee For Non Indian Nationals: 250 USD
    • Registration Fee For Indian: 6000 INR
  • Authors (Bachelors): 
    • Registration Fee For Non Indian Nationals: 200 USD
    • Registration Fee For Indian: 5500 INR

  • Listeners  (Registration without paper Publication and presentation):
    • Registration Fee For Non Indian Nationals: 70 USD
    • Registration Fee For Indian: 2500 INR
  • Who are the major sponsors:
    • Springer
    • OAJI.net
    • Cite factor
    • WZB
    • Open access library
    • EBSCO
    • Scholarsteer
    • DRJI
    • State and University library
    • Elsevier

    2. International Conference on Big Data, IoT, Cyber Security and Information Technology (ICBDICSIT), Hyderabad

    • About the conference: The conference calls for submission of original research works in the field of Data Science and discussions on the latest research work done in the area, its applications, and contribution in various industries.
    • Event Date: 2 June, 2019
    • Venue: Hampshire Plaza Hotel, 679 & 80, Lakdikapul, Hyderabad- 500004, India
    • Days of Program:1
    • Timings: 9:30 A.M to 5:00 P.M
    • Purpose: The purpose of the conference is to contribute to the scientific development in Cyber security, Big Data, and IoT by providing a platform for discussion on the latest contributions made in this area.
    • Registration cost: 
      • Academician/Practitioner/Industrialist:
        • International (Non Indian): USD 250
        • Indian: INR 650
      • PhD/Post Doc:
        • International (Non Indian): USD 200
        • Indian: INR 5500
      • Student (M-Tech/ME/Masters):
        • International (Non Indian): USD 180
        • Indian: INR 5000
      • Students (B-tech/BE/Bachelors):
        • International (Non Indian): USD 150
        • Indian: INR 4000
    • Attendees/Listener (Without paper presentation and publication):
      • International (Non-Indian): USD 100
      • Indian: INR 2000
    • Who are the major sponsors:
      • SlideShare
      • Research Gate
      • OAJI.net
      • Scribd
      • Crossref
      • Research bib
      • International journal impact factor
      • Road
      • Scholarsteer

      3. International Conference on Artificial Intelligence & Cognitive Computing, Hyderabad

      4. Practical Data Science By Industry Experts, Hyderabad

      5. Alexandria | 2 Day Course in NGS Data Analysis & Crispr, Hyderabad

      • About the conference: The conference is organised by BioDiscovery Group, India and involves lectures and demonstrations on the topics of Molecular Modeling, NGS Data Analysis, and bioinformatics.
      • Event Date: 18-19 July 2019
      • Venue: The Grand Plaza Smouha Hotel, Hyderabad
      • Days of Program: 2
      • Timings: 8:30 A.M.
      • Purpose: The conference is mainly targeted for students, but invites researchers, scientists, and faculties to impart knowledge on latest technologies including Gene Annotation, Genome ANALYSIS Database and File Formats in NGS, CRISPR for Gene editing Mechanism of CRISPR, etc.

      6. Interactive Data Science Demo, Hyderabad

      7. Data Science and Machine Learning Demystified, Hyderabad

      • About the conference: It is a masterclass by Dr. Dakshinamurthy V. Kolluru, who is the founder president and chief mentor in INSOFE, and he will discuss how data science is transforming business and management decisions around the world.
      • Event Date: 11 May, 2019
      • Venue: International School of Engineering (INSOFE), Vamsiram Builders, Hyderabad, India
      • Days of Program: 1
      • Timings: 11:00 A.M. to 12:30 P.M.
      • Purpose: This session invites working professionals, final-year graduates and postgraduates, and aspiring data scientists, to provide knowledge on application and impact of data science machine learning in various domains.

      8. Data Science Course Free Demo By Kelly Technologies, Hyderabad

      • About the conference: This session is organised by Kelly Technologies to help the aspirants understand the career scope in data science and analytics.
      • Event Date: May 12, 2019
      • Venue: Kelly Technologies, Flat no : 212, 2nd floor, Annapurna Block, Aditya Enclave, Ameerpet, Hyderabad-16
      • Days of Program: 1
      • Timings: 10:00 AM – 11:00 AM IST
      • Purpose: This session targets business managers, project managers, graduates, data analysts and other working professionals to highlight career options in data science and explore the technology and challenges in the same.
      S.NoConference nameDateVenue
      1.Data Science for Real World Conference - 2017Sunday, 22nd Oct 2017
      Hyderabad Marriott Hotel & Convention Centre, Tank Bund Road, Opposite Hussain Sagar Lake, Hyderabad, Telangana, India
      2.5th International Conference on Big Data Analytics (BDA 2017)
      December 12-15, 2017
      IIT Hyderabad, Telangana State, India

      1. Data Science for Real World Conference - 2017, Hyderabad

      • Conference City: Hyderabad 
      • About: The first ever Data Science for Real World Conference in Hyderabad aimed to reveal insights on how to optimize decision making and access commercial strategies with the help of Data Science and analytics. 
      • Event Date: Sunday, 22nd Oct 2017 
      • Venue: Hyderabad Marriott Hotel & Convention Centre, Tank Bund Road, Opposite Hussain Sagar Lake, Hyderabad, Telangana, India
      • Days of Program: One
      • Timings: 09:30 AM to 06:00 PM IST
      • Purpose: The purpose of the conference was to learn real-world case studies from experienced experts belonging to the industry and understand the role data sciences play in solving tough business issues with efficiency and reliability. 
      • Speakers & Profile: Researchers and industry practitioners.
      • Registration cost: INR 4990
      • Who were the major sponsors: Quest Learning Hyderabad, DataScience

      2. 5th International Conference on Big Data Analytics (BDA 2017), Hyderabad

      • Conference City: Hyderabad
      • About: The International Conference on Big Data Analytics provided a stage to internationally acclaimed researchers and practitioners who showcased their original case studies, experiences, and anecdotes on Big Data from various perspectives. 
      • Event Date: December 12-15, 2017
      • Venue: IIT Hyderabad, Telangana State, India
      • Days of Program: 3
      • Purpose: The Big Data conference called for papers on Data Science research and its application in various business models, including computing paradigms, data access, and analytics. 
      • How many speakers: Two 
      • Speakers & Profile:  
        • U.B. Desai, IIT Hyderabad, India 
        • P.J.Narayanan, IIIT Hyderabad, India
      • Registration cost: Free Entry

      Data Scientist Jobs in Hyderabad, India

      The ideal path to securing a job as a data scientist is as follows:

      • Getting started
      • Mathematics
      • Libraries
      • Data visualization
      • Data processing
      • Machine Learning and deep learning
      • Natural language processing
      • Polishing skills

      Getting started: Learning any programming language is the best way to start your journey as a data scientist. The most common programming languages are the R and Python programming. Having an idea of what data science is and what type of jobs it entails should be the first priority.

      Mathematics: Data science is the study of data. It requires raw data to be stored, segregated and finally interpreted, which requires both mathematics and statistics. Having good command over few of the aspects of statistics can be quite helpful in data science, like:

      • Descriptive statistics
      • Probability
      • Linear algebra
      • Inferential statistics

      Libraries: Data science is an advanced level of inventory making. Thus it not only preprocesses the data, but plots it as structured data and then uses AI algorithms on it to create databases. Some of the most popular libraries are:

      • Sci-kit learn
      • SciPy
      • NumPy
      • Pandas
      • ggplot
      • matplotlib

      Data Visualization: Having the presence of mind to categorize the raw data, finding similarities and being able to simplify the data for easy understanding is to visualize the data. One of the popular forms is graph. There are various libraries you can use to make it easier for you:

      • matplotlib-Python
      • gglpot2-R

      Data preprocessing: Data scientists start with a large mass of data that needs to be preprocessed in order to be analysis ready. The preprocessing is done with feature engineering and variable selection. After this it is fed to ML tools for analysis.

      Deep learning and ML: Machine Learning and deep learning are the medium through which data is analyzed. The preprocessed data will work only with deep learning algorithms in order to analyze such huge number of data. Both deep learning and ML are mandatory for your job application to be even considered. One should spend a few weeks reading up on CNN, RNN and neural networks.

      Natural Language processing: One should have knowledge of NLP as it helps in analyzing text form of data and classifying them as well.

      Polishing skills: There is no end to knowledge and competitions are a great way to brush up on your programming skills. Online platforms like Analytics Vidya or Kaggle have opportunities to keep working on your data science concepts. Outside online platforms you can make your own projects and study it.

      If you think you are prepared to take an interview as a Data Scientist, the following ways might help you prepare for the interview.

      Study: Reread whatever you have learnt till now. There are few things you could brush up on:

      • Probability
      • Statistics
      • Statistical models
      • Machine Learning
      • Understanding of neural networks.

      Meetups and Conferences: Going to tech summits or developer meetups will acquaint you with the people who could one day become your colleagues. This is a good way to do networking too.

      Competitions: Competitions are the best platforms to test your skills. Taking up projects to work on from Kaggle or GitHub would help polish your skills.

      Referral: Having good referrals is considered one of the most important parts of a job interview. You should always keep your LinkedIn profile updated.

      Know your Employer: Always do research on the organization you are joining. Having an idea of the type of company and values the company has will give you a new perspective. 

      Interview: Once you feel that you are ready for taking an interview, take one. Be comfortable and learn from your experience. Think of where you went wrong and how you could have answered the tricky questions differently. 

      Making data easy to infer from is the job of a data scientist. Finding patterns among structured and unstructured data, and analyzing them for the purpose of business growth will form a significant responsibility of a data scientist. In the era of virtual markets and job offerings there are a continuous flow of data that is structured and unstructured that can prove to be useful in making business decisions. The extraction of information that is appropriate for the industry will be done by data scientists.

      Basic Roles and Responsibilities of a Data Scientist are:

      1. Classifying structured and unstructured data through pattern recognition and creating database.
      2. Finding data that is relevant to the business and can be profitable from among the vast number of data.
      3. Develop Machine Learning technologies, programs and tools which will make accurate analysis of the data.
      4. Statistical analysis of appropriate data for predicting future developments of a company is also expected of a data scientist.

      Data Science is the hottest job of 21st century and is the number one profession in 2019. Due to the extreme demand for data scientists and the limited number of experts in the field, data scientists earn at least 36% higher than predictive analytics professionals. Moreover, companies like Microsoft, IBM, Amazon, Franklin Templeton, INVECAS, Thrymr Software, Woodcutter, PayZello, etc have a branch in Hyderabad and are looking for skilled data scientists. This makes Hyderabad very valuable for an aspiring data scientist. The average salary for a Data Scientist is ₹ 7,47,063 per year in Hyderabad, Telangana.

      A data scientist has the most unique position in a company. He/She will need to have an aptitude for mathematics, understands computer science and at the same time stay aware of current trends. A data scientist not only analyzes data but finds the relevant ones and directs the future of a company by predicting future outcomes.

      The following responsibilities are a part of a data scientists career graph:

      • Business Intelligence Analyst: Anyone in this position is expected to analyze the available data to understand the business and marketing trends of the industry his/her company is part of. This analysis of data is done to take the company forward by being clear about the position of a company in the business sector.
      • Data Mining Engineer: An engineer in data science has the task of analyzing data for the company as well as other third parties. Not only that, engineers are expected to optimize data analysis process by developing sophisticated algorithms.
      • Data Architect: A Data Architect's work is to make the data sources more approachable. He/She works alongside developers, system designers to integrate and protect data while finding ways of centralizing it making it more accessible.
      • Data Scientist: The data scientist works as an interpreter and idea creator by working with sets of data that correspond with particular business ventures and predicts the efficacy of it by developing hypothesis and comparing similar data. This is not all, a data scientist also develops algorithms and systems that will make analysis of data more simple and enable people to work directly with data.

      • Senior Data Scientist: Senior data scientist is expected to work with data in order to predict the future of a company. He/She should create projects and develop systems in the present with an eye towards the future so that the conditions of a company can be predicted.
      • Data Science Hyderabad
      • TRUE DATA SCIENCE
      • Hyderabad Data Science Group
      • Hyderabad Data Science & AI Meetup
      • Hyderabad Data Science Professionals (HYDSP)

      There are various ways one can look for possible employees:

      1. Through Data Science conference
      2. Online platforms like LinkedIn
      3. Social gatherings like Meetup

      Being the most popular career choice of 2019 there are various career opportunities of a Data Scientist-

      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

      Elite companies like Microsoft, IBM, Amazon, Franklin Templeton, INVECAS, Thrymr Software, Woodcutter, PayZello, etc have branches in Hyderabad and are in search of data science professionals. Below are the key points on which every data scientist is evaluated for being considered as a potential employee.

      1. Education: Since data science requires sophisticated level of interpretation having higher level education is always a criteria. Data scientists are considered to hold the most number of PhDs. Even getting certified can also help in getting employment.
      2. Programming: Programming is a crucial part of data science. Being well-versed in R and Python programming languages are a must for any data scientist as most of the work is done through these.
      3. Machine Learning: It is ML and deep learning that analyzes data to find patterns and relationships after they have been prepared. Machine learning is imperative to any data science projects.
      4. Projects: Companies look for hands on experience of data scientists. Unless you have practical knowledge of what you have theoretical notions of, your education is not complete. Thus, projects are a good way of providing an understanding of your capabilities and also adds to your resume.

      Data Science with Python Hyderabad, India

      • Python is a versatile multi-faceted programming language: Python is easy to use and comes with various libraries and packages that are useful to data scientists. It is structured and object oriented making it perfect for data science.
      • Python is the most simple and readable programming language that instantly attracts data scientists. It comes with appropriate analytic libraries and tools that are ideal for the kind of work done in data science. It is unique in relation to any other programming language making it the only choice for all data science projects.
      • The diversity of resources available on Python makes it a safe option for data scientists. Even if faced with a problem data scientists have a broad area of resources available to quickly work up a solution while working on a Python program or developing a data science model.

      • Another advantage of using Python is the availability of a community of developers using the same programming language. Python being the most popular programming language, the number of people working on it is high. Since the type of work being done with Python by data scientists are similar, if anyone faces difficulty at any point with their projects it is probable that someone else in the community faced the same problem and has already found a solution. Being part of a community with the most number of people makes it easier to brainstorm solutions. And people working with Python are always helpful.

      Data Science is a huge field which requires working with a large number of libraries. Finding the right programming language to master is, therefore, important to work efficiently with all the libraries-

      R programming: The only challenge of R is its steep learning curve, but it is an important language for various reasons

      • It has a huge open-source community that provides numerous high quality open-source packages for R.
      • It boasts of smooth handling of matrix operations and has large statistical functions.
      • It has included with it ggplot2 that enables data visualization

      Python: With lesser packages than R, Python is still considered to be popular with data scientists. The reasons for that is-

      • Libraries like pandas, scikit-learn and tensorflow equip Python to provide most library needs for data science purposes.
      • It is very easy to use and operate.
      • It has an open-source community that is considered one of the largest one.

      SQL: Working on relational databases, Structured Query Language has-

      • Readable syntax
      • Efficiency in updating, manipulating and querying data for relational databases.

      Java: One of the oldest programming languages, Java has limited libraries limiting its potential. Nevertheless it has some advantages.

      • Systems coded with Java at the backend makes it easier to integrate data science projects with it making it a compatible option.
      • It is a high performance, general purpose, compiled language.

      Scala: Working on JVM, it is considered rather complicated. But it does have some advantages-

      • Running on JVM, Scala can run on Java as well.
      • Used alongside Apache Spark it enables high performance computing cluster.

      The following are the steps to downloading Python 3 for Windows:

      Download and 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.

      Alternatively, you can also install python via Anaconda 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

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

      You can simply install python 3 from their 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, just follow the below steps:

      • 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 the following command: 

      /usr/bin/ruby -e “$(curl -fsS https://raw.githubusercontent.com/Homebrew/install/master/install)”Confirm if 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

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

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      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

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      Data Science with Python Certification Course in Hyderabad

      Hyderabad is the capital of the southern Indian state of Telangana and the de jure capital of Andhra Pradesh. Endearingly called the Pearl City, Hyderabad offers a variety of tourist attractions ranging from Heritage monuments, Lakes and Parks, Gardens and Resorts, Museums to scrumptious cuisine and an amazing shopping experience. It is as much known for its Charminar as the Taj Mahal is of Agra or the Eiffel Tower is of Paris. Set up in 1591 by Muhammad Quli Qutb Shah, Hyderabad stayed under the rule of the Qutb Shahi dynasty for nearly a century before the Mughals captured the region. In 2014, the newly formed state of Telangana split from Andhra Pradesh and the city became joint capital of the two states, a transitional arrangement scheduled to end by 2025. The city has a vibrant software industry and offers immense scope for software professionals in Agile & Scrum, Project Management, IT Security etc, beside others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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