Data Science Course with Python in Bangalore, India

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

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

Grow your Data Science skills

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

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Highlights

  • 42 Hours of Live Instructor-Led Sessions

  • 60 Hours of Assignments and MCQs

  • 36 Hours of Hands-On Practice

  • 6 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

Data Scientists are in high demand across industries

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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

Bangalore is known as the ‘Silicon Valley of India’. Moreover, it is the most technologically advanced city of the country. It has the most prominent institutes and has some of the biggest companies such as LinkedIn, Amazon, WyngCommerce, Vedantu, Michael Page, Citi, etc. Data scientists have become a necessary asset in every organization in recent times. Although there is no concrete definition of Data science, its impacts around us can be noticed significantly. Data Science can be summarized in five stages of its life cycle which includes the following:

  • Capture
  • Maintain
  • Process
  • Analyze
  • Communicate

According to reports from LinkedIn, the data scientist is listed as one of the most promising jobs in 2017, 2018 and 2019. Some of the common data scientist job titles are as follows:

  • Data Architect
  • Data Analyst
  • Data Scientist
  • Data/Analytics Manager
  • Business Intelligence Manager
  • Data Administrator

The reasons for the popularity of Data Science as a career choice are as follows:

  • Supply-Demand gap: Companies demand that data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts effectively. Data science is evolving fast and has already shown such enormous range of possibility, that the demand is likely to increase in the future as well. Since the data is highly important, it is a priority for the companies that the data scientists they hire are professionals and skilled. Owing to the increasing demand and shortage of supply, a gap is created which also leads to an increase in salaries of data scientists.
  • Revolutionize the organizations
    • All these search engines like Google, Yahoo, Bing, Ask, AOL, Duckduckgo, etc. make use of data science algorithms to deliver the best result for our searched query in a fraction of seconds. The fact that Google processes more than 20 petabytes of data every day shows the significance of data science.
    • Digital ads can be targeted based on the user's past behavior and this is the reason why they have been able to get a lot higher CTR than traditional advertisements.
    • The recommendations on almost all the websites including internet giants like Amazon, Twitter, Google Play, Netflix, Linkedin, imdb are made based on previous search results for a user.

These are just a few examples from our day to day life to show how data science is involved in all aspects. Apart from this, it has revolutionized healthcare significantly, opening new areas of research and discoveries. Its contribution to other fields like wildlife, weather forecast, banking sectors, etc. is also significant.

  • Salary opportunities: Due to the high demand for Data scientists, the salary is high and is estimated to rise in the future as well. Gaining specialized skills in Data science is a bonus since it can be rewarding. Skills like machine learning, artificial intelligence, high-level programming will bring you more income. Start-ups in India are paying average salaries of Rs. 10.8 lakhs to data scientists which is 12.5 % higher than the average salaries paid by their larger counterparts.

Bangalore is home to some of the most prestigious universities in the world in terms of Data science courses. These institutions include INSOFE, International Institute of Information Technology, IIK (Indian Institute of Knowledge hub), Peopleclick, Data Science, Data scientist & Data Analytics Courses, Business Analytics Training Institute Bangalore, Indian Institute of Management Bangalore, etc. The top skills that are needed to become a data scientist include the following:

  1. Programming
  2. Big Data
  3. Statistics
  4. Machine Learning and Advanced Machine Learning
  5. Data Cleaning
  6. Data Ingestion
  7. Data Visualization
  8. Unstructured data

1. Programming:

Data Science is a dynamic field with ever increasing tools and technologies added to it every now and then. You should be able to choose the best programming language suited to you to tackle a specific kind of problem. Apart from mathematical skills, it is important to be proficient in one or more programming languages. The programming for Data Science differs from the conventional programming language in the sense that it helps the user to pre-process, analyze and generate predictions from the data, while the other programming languages focus on software development. The main programming languages that an aspiring data scientist should be familiar with are as follows:

  • R
  • Python
  • SQL
  • Scala
  • Julia
  • SAS

2. Big Data:

Big Data technology centers in ways to analyze a large volume of data to reveal behavior, trends, and patterns especially related to human behavior. Big Data Analytics is in the frontiers of IT as it aids in improving business, decision making and providing the biggest edge over the competitors hence it is crucial. Therefore, it is very important to have knowledge about frameworks like Hadoop and Spark that can process Big Data.

Apache Spark is a fast and general-purpose cluster computing system designed to cover a wide range of workloads such as iterative algorithms, interactive queries, batch applications, and streaming. Hadoop provides scalable, reliable, and distributed computing to solve problems including huge amounts of data.

3. Statistics:

Statistics is a broad field which is defined by Wikipedia as the study of the collection, analysis, interpretation, presentation, and organization of data. The minimum skills needed to make better business decisions from data are descriptive statistics and probability theory. Machine learning requires understanding Bayesian thinking which is the process of updating beliefs as additional data is collected. Key concepts in statistics include:

  • probability distributions 
  • statistical significance
  • hypothesis testing
  • regression.

4. Machine Learning and Advanced Machine Learning:

Machine Learning focuses on the development of computer programs in such a way that they can access data, analyze it and manipulate it so that it provides the ability to systems to automate the experience without the need of programming. Machine Learning requires a better understanding of neural networks, reinforcement learning, adversarial learning, etc. and can be considered as a subset of Artificial Intelligence. The different types of Machine Learning techniques include the following:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

It is recommended to have good knowledge of various Supervised and Unsupervised learning algorithms such as:

  • Random Forest
  • Clustering (for example K-means)
  • Logistic Regression
  • K Nearest Neighbor
  • Linear Regression

5. Data Cleaning:

Since the data that the data scientists work on is highly sensitive and important, it is important that the data is correct and accurate before data scientists analyze it and therefore, a considerable amount of time and effort is spent to ensure this. Incorrect or inconsistent data leads to false conclusions hence it has a high impact on the quality of the results. Data quality is defined as validity, accuracy, completeness, consistency, and uniformity of data. The workflow followed for data cleansing includes the following steps:

  • Inspection
  • Cleaning
  • Verification
  • Reporting

6. Data Ingestion:

Data Ingestion is the process of accessing and importing data from several different sources into our system for analytics. The sources of data are your IoT Smartwatch, social networks, customer portals, messengers, forums, etc. These are the most common examples of data ingestion :

  • HTTP POST 
  • Download file from FTP

       The different data ingestion tools available :

  • Apache Flume
  • Apache NIFI
  • Syncsort
  • Apache Flume
  • Apache Kafka
  • Gobblin
  • Heka    

7. Data visualization:

Data visualization tools provide a better and accessible way to enable decision-makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. It helps to see and understand trends, outliers, and patterns in data by using visual elements like maps, graphs, and charts. By using technology to drill down into charts and graphs for more detail, we can interactively change what data you see and how it’s processed through visualization. A good and effective data visualization tool makes large data sets coherent and some of these tools are as follows:

  • Tableau
  • Infogram
  • ChartBlocks
  • Datawrapper
  • Plotly
  • RAW
  • Visual.ly

8. Unstructured Data:

Unstructured data can be defined as data that cannot fit neatly into a database and does not have a recognizable structure. It does not follow the conventional data model like Word documents, email messages, PowerPoint presentations, survey responses, transcripts of call center interactions, and posts from blogs and social media sites. Therefore, it leads to ambiguities that are difficult to identify using conventional software programs. Working with unstructured data provides a better insight into analyzing data.

Below are the top 5 behavioral traits of a successful Data Scientist in Banglaore -

  • Innovative – Since data science offers huge scope for imagination and creativity, there is always a space for new additions. It demands a person to think out of the box and get off the track of conventional ways.
  • Business Understanding – It is important for a data scientist to understand the business's needs and develop analytics that meets those objectives. He must clearly understand the intentions and goals of the business and list down the requirements accurately. The business rules and limitations should be a thing to keep in mind while working.
  • Teamwork – A data scientist does not work alone. He works with a team of people from different sectors including business, finance, marketing, technology, etc. and each of the members of the team contributes to the development of the work going on. It is important to understand the team spirit and maintain a jovial and professional bond with the people. 

  • Passion – Data science is an art just like science. A data scientist must be driven by a passion to make a change or contribute to technology through his work. Mediocre solutions are available in great amounts everywhere, but data scientists should aim at something unconventional and off the track.

A Harvard Business Review article labeled “data scientist” as the sexiest job of the 21st century. Some of its benefits can be summarised as follows:

  1. Highly paid career: According to a report by Glassdoor, the national average salary for a Data Scientist is ₹10,00,000 in India. It is estimated that the salaries will rise in the near future since there is an increasing demand for skilled data scientists while the supply is still low. 
  2. Abundance of positions: Data Science is less saturated as compared with other IT sectors since there are very few people who have the required skill-set to become a complete Data Scientist. Therefore, data science is highly in demand. Also, the reason is the vast expansion of this technology. There is also much potential scattered across job outlook in the fields of government, academia, retail, healthcare, banking, and gaming.
  3. Stable career choice: The outlook for growth in the field of data science is blooming and there is room for this growth, unlike IT jobs where a technology stays in the market for a while and then fades away.  Data Science is a vastly abundant field and has a lot of opportunities.
  4. Freedom to work: A lot of people are still not entirely aware of the multitude of possibilities that are correlative to being a data scientist. Apart from the contribution to technology, its work in the corporate setting, marketing, consulting, healthcare, and financial services is also promising. With time, we are witnessing new additions to these technologies. It has brought about a revolution in every field it is being implemented in. Data Science is an ever-evolving technology. There is always room for growth. A data scientist has the privilege to think outside the box and go beyond the conventional ways and methods of finding solutions.
  5. Opportunities to network: Since many innovations are added to Data Science from different researchers, professionals, and scientists, many conferences and meetups are organized to bring them together and share their ideas. Some people come from strong backgrounds and holds a reputable position in their domains. It is a great opportunity to learn and grow, apart from this, you also get to understand a different work culture, and you create a strong professional network.

Data Scientist Skills & Qualifications

Below is the list of top business skills needed to become a data scientist. These skills are a must whether you live in Bangalore or Mumbai: 

  1. Critical Thinking
  2. Communication Skills
  3. Intellectual Curiosity
  4. Industry Knowledge

1. Critical Thinking – Critical thinking involves deliberately and systematically processing information so that you can make better decisions. The role of a data analyst is to uncover and synthesize connections that are difficult to understand. 

2. Communication Skills – Data Scientists need to convey their ideas and solutions to other people in a language that is easily understood by everyone. He/She must possess good communication skills. Most of the presentation is done in the form of charts, graphs, figures, and statistics. It is important to simplify it since a team includes people from different areas.

3. Business acumen  – The business requirements of different companies are different. It depends on a number of factors. The solutions or ideas proposed by you affects the business, sometimes on a very broad scale. Therefore, it is important for you to know the objective of the business and the impact you are going to create through your contributions.

4. Presentation Skills – A data scientist works in a team of people with different roles. He/ She needs to deliver a speech or a presentation in front of his/ her team, clients, or any stakeholder. Therefore, it is important to have good presentation skills.

The 5 best ways to brush up your Data Science Skills to get a Data Scientist job are as follows:

  • Online courses: Online courses take time to impart all the knowledge but investing a good amount of time helps you to dive deeper into this technology and provides you with an edge in learning. Some of the best online courses available are as follows:
    • KnowledgeHut
    • Python for Data Science and Machine Learning Bootcamp — Udemy
    • Data Science Specialization — JHU (Coursera)
    • Applied Data Science with Python Specialization — UMich (Coursera)
    • Dataquest
    • CS109 Data Science — Harvard
    • Introduction to Data Science — Metis
  • Boot camps: Different career paths will require some level of programming, data visualization, statistics, and machine learning knowledge and skills. The top Data Science boot camps to help you start your career in Data Science are as follows:
    • RMOTR
    • DataCamp
    • Metis
    • Level
    • Springboard
    • Jedha
    • The Dev Masters
    • General Assembly
  • Certifications: Certifications are a great way to gain an edge because they allow you to develop skills that are currently in demand in your desired industry. Below are some of the certifications that you can take:
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
    • Certified Analytics Professional (CAP)
    • Dell Technologies Data Scientist Advanced Analytics Specialist (DCS-DS)
    • Data Science Council of America (DASCA)
    • Microsoft MCSE: Data Management and Analytics
    • Microsoft Certified Azure Data Scientist Associate
    • SAS Certified Advanced Analytics Professional
    • SAS Certified Big Data Professional
    • Cloudera Certified Professional: CCP Data Engineer
  • Research papers and mini projects: Many projects are available to take up online. You can refine your search by choosing your level of difficulty, starting from beginner to proficient, based on your knowledge and confidence with the tools and technology. Such projects will boost your profile and help you get closer to your desired job.  
  • Competitions and hackathons: Once you have developed confidence after attending suitable courses, boot camps, and conferences, you can test yourself by participating in Hackathons and Machine learning Competitions. It provides an opportunity to judge yourself and assess where you lie in the race. Apart from all this, it is a great opportunity to learn. Some of the competitions available are:
      • Kaggle
      • Driven Data
      • CrowdANALYTIX
      • Innocentive
      • TunedIT
      • Codalab

In India, Data Science is a lucrative career option. Every sector and organization is inviting candidates based on their requirements. Bangalore has some of the biggest companies such as LinkedIn, Amazon, WyngCommerce, Vedantu, Michael Page, Citi, etc which offer jobs to Data Science professionals.

The kind of companies that employ data scientists are as follows:

  • Small companies use Google Analytics for their analysis as they have fewer resources and fewer data to work with.
  • The demand for data scientists in government agencies is on par with the demands in the private sector in India with many new openings in Big Data and related field.
  • Multinational companies work on a very bigger and broader range of data, and they need data scientists for different roles.
  • You can work on some of the projects based on Python. There are many online resources available. One such example is Harvard CS 109. They have some problem sets that you can work on. They come with solutions and video lectures as well.
  • You can find large datasets open to the public like the following:
  • You can practice some projects yourself. Here is the list of 5 topics that you can take up to practice your data science skills:

    • Data Cleaning
    • Exploratory Data Analysis
    • Machine Learning
    • Interactive Data Visualization
    • Communication

How to become a Data Scientist in Bangalore

The three general and basic steps to become a data scientist are as follows:

  1. You must have a bachelor's degree in computer science, IT, math, physics, or another related field;
  2. This is not must but a bonus to have a master's degree in data or related field;
  3. Gain experience in the field you intend to work in to understand the basic functioning (ex: healthcare, physics, business).

Next, you should focus on to develop an in-depth skill and knowledge in all or some of the following technologies:

  • Machine Learning
  • Programming/Software
  • Hadoop Platform
  • Statistics/ Mathematics
  • Artificial Intelligence
  • Data Cleaning
  • Data Munging
  • Data Visualization
  • Unstructured data

The job of a data scientist is challenging and highly in demand. There are different skill sets required by different companies still there are some general steps to be followed by everyone who aspires to become a data scientist.

1. Degree: You must hold a basic engineering or related degree in Computer Science, IT, Mathematics, etc.

2. Certificate: It is advised to get certification in order to enhance your profile. It also confirms that you are proficient enough in a particular field. Below are some of the certifications available:

  • Data Science Council of America (DASCA)
  • Certified Analytics Professional (CAP)
  • Cloudera Certified Professional: CCP Data Engineer
  • Applied AI with DeepLearning, IBM Watson IoT Data Science Certificate
  • SAS Certified Advanced Analytics Professional
  • Microsoft Professional Program in Data Science

3. Technical skills: You must aim to master one or more of the most emerging technologies of Data Science. You can choose this aspect based on your interest and your desired job profile. These technologies are as follows:

  • Programming/Software
  • Machine Learning 
  • Statistics/ Mathematics
  • Artificial Intelligence
  • Data Munging
  • Data Cleaning
  • Unstructured data
  • Data Visualization

There are many advantages to having a degree in Data Science. A data scientist with degree can-

  • gather statistical and data intuition through their work
  • think critically about hard problems
  • do research involving programming and large datasets
  • show resilience when asking/answering hard questions
  • learn and adapt quickly

Generally speaking, it is not an essential criterion but still, there are certain points to be taken care of. You can go for a Master’s degree based on the role and the company that you are focusing on.

  • 'Product’ Data Science Role

If the product of the company is solely based on Data Science, then the expectations are really high and such companies demands for a Master’s degree. The role of the data scientist is very crucial in such companies. For example, in cybersecurity, fraud detection is based on Data Science. If you wish to work in data-based companies like Google and Facebook, then having a Master’s degree is a bonus.

  • ‘Insight’ Data Science Roles

In some companies, Data Science is used as a way to provide insights to other teams or to enhance the core product like the product, sales, and marketing teams. For example, a company like Target use data science to predict how much inventory to stock in different stores. In such companies, the Master’s degree is not a necessary factor.

A programming language is a key skill that a Data Scientist must possess. You must be proficient in one or more of the following programming languages.

  • R
  • Python
  • Java
  • SQL
  • Julia
  • Scala
  • TensorFlow
  • MATLAB

Data Scientist Salary in Bangalore

The average annual salary of a Data Scientist in Bangalore is Rs. 6,15,496.

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

A data scientist in Bangalore earns about Rs. 6,15,496 every year as compared to Rs. 6,72,492 in Mumbai.

The annual earnings of a data scientist in Bangalore is Rs. 6,15,496 as compared to Rs. 8,19,815 in Chennai.

There is a high demand for Data Scientists in Bangalore. Lot of companies are trying to leverage the abundant data that is being generated each day and this has created huge job opportunities for Data Scientists in Karnataka. 

Data Scientist is one of the hottest jobs right now. If you are a data scientist in Bangalore, you will get several opportunities to work and grow in your career owing to the presence of major players like Accenture, Infosys, etc. and also the numerous startups that are present here.

Bangalore is the best place to work if you are looking for growth. The city is home to several startups that offer multiple opportunities to freshers as well as experienced employees. Data Scientists also get to gain the attention of executives as they play a key role in determining useful business insights. In this field, many certifications are not required as you will learn on the job with time. Also, a data scientist is not bound to work for a particular business alone. You can use this new technology with enormous potential in any field that interests you.

The companies hiring Data Scientists in Bangalore are DigiSciFi Technologies, SAP Labs India Pvt Ltd, Intellicar Telematics Pvt Ltd, Accenture Solutions Pvt Ltd, People Source Consulting Pvt Ltd, and many more.

Data Science Conferences in Bangalore

S.NoConference nameDateVenue
1.Machine Learning Developers Summit 2019, Bengaluru, India30-31 January, 2019NIMHANS Convention Centre, Hosur Main Road, Lakkasandra, Hombegowda Nagar, Bengaluru, Karnataka 560029
2.Open Data Science Conference, Bengaluru, India7-10 August, 2019Sheraton Grand Bangalore Hotel, A Block, 26/1, Dr. Rajkumar Rd, Rajaji Nagar, Bengaluru, Karnataka 560055
3.Data Platform Summit 2019, Bengaluru22-24 August, 2019Hotel Radisson Blu (Formerly Park Plaza), 90/4, Marathahalli Outer Ring Road, Bengaluru, Karnataka 560037, India
4.Future of Analytics Summit 2019, Bengaluru, India27 February, 2019The Ritz-Carlton, 99, Residency Rd, Shanthala Nagar, Ashok Nagar, Bengaluru, Karnataka 560025
5.Great International Developers Summit, Bengaluru, India22-25 April, 2019IISc Bengaluru, National Science Seminar Complex, CV Raman Rd, Kodandarampura, Malleshwaram, Bengaluru, Karnataka 560012, India
6.The Fifth Elephant, Bengaluru, India25-26 July, 2019

NIMHANS Convention Centre, Hosur Main Road, Lakkasandra, Hombegowda Nagar, Bengaluru, Karnataka 560029

7.CYPHER 2019
18-20 September, 2019
TBA
8.Artificial Intelligence and Machine Learning Summit 2019 - Bangalore
23 May, 2019
Hyatt Centric Mg Road Bangalore, Swamy Vivekananda Road, Someshwarpura, Ulsoor, Bengaluru, India
9.Bengaluru Tech Summit 2019
18-20 November, 2019

Bengaluru Main Palace, Bengaluru, Karnataka, 560052, India

1. Machine Learning Developers Summit 2019, Bengaluru, India

  • About the conference: The conference includes keynotes, workshops, and sessions in machine learning and highlights both the business and technical aspects of machine learning.
  • Event Date: 30-31 January, 2019
  • Venue: NIMHANS Convention Centre, Hosur Main Road, Lakkasandra, Hombegowda Nagar, Bengaluru, Karnataka 560029
  • Days of Program: 2
  • Timings: 9 A.M. to 6 P.M.
  • Purpose: Learn about the deployment and production of the latest machine learning framework, and discuss the software architecture of the ML system.
  • Speakers & Profile:
    • Rahul Lodhe - Director Engineering at SAP Labs India Pvt Ltd
    • Madalasa Venkataraman - Chief Data Scientist at TEG Analytics
    • Ashok Veilumuthu - Datascience Expert at SAP Labs India Pvt Ltd
    • Ranjit Chansarkar - Senior Consultant - Analytics at bigbasket.com
    • Kavitha Krishnan - Sr. Product Manager at SAP Ariba Analytics
    • Milind Hanchinmani - Director, Asia Pacific & Japan, Developer Relations Division, Core and Visual Computing Group at Intel
    • Srinivasan Govindaraj - Director and Head of Data Sciences at Happiest Minds Technologies
    • Suresh Srinivasan - Co-Founder at FORMCEPT Technologies
    • Praveen Srivatsa - Director at Asthrasoft Consulting
    • Reena Sethy - Director Product Management at SAP Labs India Pvt Ltd
    • Sandeep Alur - Director - Partner Technology Engagements at Microsoft
    • Vijoy Basu - Senior Director – Data and Analytics at Cognizant
    • Ashutosh Rastogi - Product Manager at SAP
    • Rohit Patel - Manager at Mphasis Limited
    • Pragati Ogal Rai - Director-Developer Audience at Microsoft
    • Ashokkumar KN - Sr Product Manager at SAP Analytics
    • Prabhavathy Adavikolanu - Director – Product Development, New Business Opportunities at Intel India
    • Ananth Nagaraj - Co-Founder at Gnani.AI
    • Ramu Timma Gowda - Dr. Product Management at SAP Labs India Pvt Ltd
    • Chiranth Ramaswamy - Senior Director Consulting at Capgemini
    • Ravinder K Sharma - Global Sr. Director - Analytics, Insights & New Capabilities at AB InBev
    • Amitabh Mishra - CTO at Emcure Pharma
    • Saurabh Chandra - Engineering Leader at Amazon India Retail Business
    • Indranath Mukherjee - Head - Strategic Analytics at AXA XL
  • Registration cost:
    • Early Bird Pass: ₹6,500 + taxes
    • Regular Pass: 8,000 + taxes
    • Late Pass: 10,000 + taxes 
  • Who are the major sponsors:
    • Microsoft
    • Intel Software
    • Amazon Web services 
    • Oracle cloud solution hub
    • Amity University online
    • SAP
    • Insofe
    • Capital One
    • ABInBev
    • Crediwatch
    • Nikhil Analytics
    • IIIT Hyderabad

    2. Open Data Science Conference, Bengaluru, India

    • About the conference: The conference imparts knowledge on leading industry practices and tools used in data science.
    • Event Date: 7-10 August, 2019
    • Venue: Sheraton Grand Bangalore Hotel, A Block, 26/1, Dr. Rajkumar Rd, Rajaji Nagar, Bengaluru, Karnataka 560055
    • Days of Program: 4
    • Timings: 09:00 AM-06:00 PM
    • Purpose: The conference provides a platform to connect with leading innovators in the field of data science to explore related tools, topics, languages and much more.
    • Speakers & Profile:
      • Grant Sanderson - Creator of 3Blue1Brown(YouTube channel)
      • Viral B. Shah - Co-founder and CEO, Julia computing language
    • Registration cost: 
      • Early bird: Rs. 5000
      • Smart: Rs. 7000
      • Regular: Rs. 9000
      • Late: Rs. 12,000
      • Last minute: Rs. 15,000

    3. Data Platform Summit 2019, Bengaluru, India

    • About the conference: The conference brings together SQL experts and industry leaders to deliver advanced sessions in Data Science.
    • Event Date: 22-24 August, 2019
    • Venue: Hotel Radisson Blu (Formerly Park Plaza), 90/4, Marathahalli Outer Ring Road, Bengaluru, Karnataka 560037, India
    • Days of Program: 3
    • Purpose: The conference highlights seven technology tracks which include Business Intelligence & Advanced Analytics, Data Science,  IoT & NoSQL, Database Administration, Open Source, Database Development, and Cloud.
    • Speakers & Profile:
      • Peter Myers - Independent BI Expert and Data Platform MVP from Australia
      • Reid Havens - Independent BI Expert and Data Platform MVP from the USA
      • Anupama Natarajan - Data and AI Consultant, Data Platform MVP, MCT from New Zealand
      • Sandy Winarko - Principal Program Manager, Microsoft China
      • Damian Widera - Sr. Project Manager, MVP, MCT from Poland
      • Hamish Watson - Consultant, DevOps Enthusiast, MVP from New Zealand
      • Joey D’antoni - Senior Consultant, Data Platform MVP from the USA.
      • Amit Bansal - Data Platform Architect, Data Platform MVP, Microsoft Certified Master, Microsoft Regional Director from India
      • Siva Harinath - Principal Program Manager, Microsoft Redmond
      • Denny Cherry - Principal Consultant, Data Platform MVP, Microsoft Certified Master of SQL Server from the USA.
    • Registration cost: 
      • Summit only: INR 12,500+18% GST
      • 1 pre-con only: INR 12,500 + 18% GST
      • 2 pre-cons only: INR 23,750+ 18% GST
      • 3 Pre-cons only: INR 33,750+ 18% GST

      4. Future of Analytics Summit 2019, Bengaluru, India

      • About the conference: The conference invites leaders and researchers in the field of data science to come together and share their innovative ideas, latest and upcoming technologies, and tools in analytics.
      • Event Date: 27 February, 2019
      • Venue: The Ritz-Carlton, 99, Residency Rd, Shanthala Nagar, Ashok Nagar, Bengaluru, Karnataka 560025
      • Days of Program: 1
      • Purpose: The purpose of the conference is to provide a platform to share ideas and insights on analytics in order to promote business growth.
      • Speakers & Profile:
        • Ravi Vijayaraghavan - Vice President and Head - Analytics and Decision Sciences Flipkart.com
        • Himanshu Goyal - India Business Leader, The Weather Company, an IBM Business
        • Jitendra Mahapatra - AVP and Head - Digital & Payments Analytics, Axis Bank
        • Guruprasad Mandrawadkar - VP - Head of Business Intelligence Star India
        • Upali Basu - Practice Head,  Analytics IndiGo (InterGlobe Aviation Ltd)
        • Somu Vadali - Digital and Analytics Expert in Retail and FinTech Future Group
        • Karthik Venkatraman - Head for Solutions Informatica India
        • Manisha Banthia - Director - Analytics CoE, Fiserv
        • Kunal Mhaske - Head Of Analytics, Piramal OTC
        • Ashish Singru - Senior Director, Finance & Analytics, eBay
        • Gautam Mehra - Chief Data Officer, Dentsu Aegis
        • Dr. Ashutosh Misra - Director of Advanced Analytics and Big Data Philips Lighting
        • Tanmay Agarwal - VP & Head-Global Business Services Hindustan Coca-Cola Beverages Pvt Ltd
        • Vidhya Veeraraghavan - Associate Vice President – Analytics, Standard Chartered global business services, Pvt Ltd.
        • Bharath Shasthri - Vertical Head ML DL & Big Data, HDFC bank
        • Ashutosh Tripathy - Head of Business Line, Customer Due Diligence & Data Royal Bank of Scotland
        • Ranjan Agarwal - AVP analytics & strategy, Lenskart.com
        • Karthik Kumar - Head- Marketing Analytics, Amazon
        • Arpita Patnaik - Data science Leader, Digital R&D, Unilever
      • Registration cost: 
        • Indian delegates: INR 15,000
        • International delegates: $500
      • Who are the major sponsors:
        • The Weather Company
        • Informatica
        • Fiserv
        • Impetus

      5. Great International Developers Summit, Bengaluru, India

      • About the conference: The conference provides a platform for innovators and entrepreneurs to share their insights on emerging technologies on data and cloud, dynamic language and mobile technologies.
      • Event Date: 22-25 April, 2019
      • Venue: IISc Bengaluru, National Science Seminar Complex, CV Raman Rd, Kodandarampura, Malleshwaram, Bengaluru, Karnataka 560012, India
      • Days of Program: 4
      • Purpose: The conference covers topics on AI, software architecture, cloud, data, mobile technologies, software architecture, JAVA, and dynamic languages.
      • Speakers & Profile:
        • Dr. Venkat Subramaniam - instructional professor at the University of Houston
        • Scott Davis- Principal Engineer with ThoughtWorks
        • John Bruce - Co-founder and CEO of Inrupt
        • Michael Carducci - Software Engineer
        • Abhinav Shroff - Principal Product Manager at Oracle
        • Guru Bhat - General Manager, PayPal
        • Amitpal Singh - Director of Oracle Labs
        • Apoorv Dalal - Engineering Director/Head for Uber
        • Charu Srinivasan - Partner Director of Engineering in the Cloud & AI Division at the Microsoft India Development Center (IDC)
        • Iddo Gino - CEO of RapidAPI
        • Nick Tran - Vice President of Developer Relations at Akamai 
        • Rajesh Chitharanjan - Chief Technologist for Publicis Sapient's the Middle East and North Africa Region 
        • Rakesh Ravuri - CTO, SVP Engineering and Global Retail Engineering Lead at Publicis Sapient
        • Pradeep Balachandran - Program Director for IBM Cloud DevOps and Eclipse Strategy & Development in IBM 
        • Yasufumi Hirai - Group Executive Vice President, CIO and CISO (Chief Information Security Officer) of Rakuten, Inc.
        • Stuart Rimell - Head of Future Design at IG
        • Patrick Chanezon -  Principal Cloud Advocate at Microsoft
        • Pavan Podila -  Google Developer Expert for Web Technologies
        • Matt Raible -  Java Champion, Web Developer, and Developer Advocate at Okta
        • Aiko Klostermann - consultant and developer for ThoughtWorks
        • Caven Cade Mitchell - Founder of DevJapan and DesignJapan
        • Chandra Guntur - Director and Java Advocate in Resilient Systems Engineering, BNY Mellon
      • Who are the major sponsors:
        • PayPal
        • Microsoft Azure
        • IBM
        • Uber
        • Oracle
        • Rakuten
        • ThoughtWorks
        • Tesco
        • Finland Works
        • Akamai Developer
        • Publicis sapient
        • Plivo

        6. The Fifth Elephant, Bengaluru, India

        • About the conference: The conference brings together experts in Data Science to discuss the latest developments in data science. 
        • Event Date: 25-26 July, 2019
        • Venue: NIMHANS Convention Centre, Hosur Main Road, Lakkasandra, Hombegowda Nagar, Bengaluru, Karnataka 560029
        • Days of Program: 2
        • Purpose: The conference aims to cover the topics on machine learning, IoT, data engineering, and building data-driven products.
        • Registration cost: INR 7,200
        • Who are the major sponsors:
          • Salesforce
          • Freshworks
          • Publicis sapient
          • GoJek
          • Sumo Logic

          7. CYPHER 2019,Bengaluru, India

          • About the conference: The conference is a platform for discussion of latest and innovative ideas in AI, Artificial Intelligence, Big Data, and Data Science, and to discuss the challenges faced in the relevant technology.
          • Event Date: 18-20 September, 2019
          • Venue: TBA
          • Days of Program: 3
          • Timings: 9 A.M. to 5:30 P.M.
          • Purpose: The conference aims to connect the analytics community to discuss the technological advancements in Data Science, AI, Big Data, and Analytics.
          • Speakers & Profile:
          • Registration cost: 
            • Super Early Bird: INR 10,000
            • Early passes: INR 12,500
            • Regular passes: INR 15,000
          • Who are the major sponsors:
            • Jigsaw Academy
            • University of Chicago
            • Analytix Lab
            • IBM
            • ZS
            • Insofe
            • The Weather Company
            • SAP
            • Unlimit
            • Times Professional Learning
            • International Institute of Digital Technologies
            • Great learning

            8. Artificial Intelligence and Machine Learning Summit 2019, Bengaluru, India

            • About the Conference: The conference aims to simplify the concepts and applications of intelligent technologies for technology and business executives to accelerate innovative efforts.
            • Event Date: 23 May, 2019
            • Venue: Hyatt Centric Mg Road Bangalore, Swamy Vivekananda Road, Someshwarpura, Ulsoor, Bengaluru, India
            • Days of Program: 1
            • Timings: 8:45AM - 4:30 PM (IST)
            • Purpose: The purpose of the conference is to improve innovative efforts by simplifying the concepts and applications of intelligent technologies.

            9. Bengaluru Tech Summit 2019

            • About the conference: The theme of the conference is ‘Innovation and Impact’ and it provides a platform for discussion on the latest technologies and their impact on daily life.
            • Event Date: 18-20 November 2019
            • Venue: Bengaluru Main Palace, Bengaluru, Karnataka, 560052, India
            • Days of Program: 3
            • Purpose: The conference aims at exploring the upcoming technologies and challenges through many events like startup showcase, B2B trade show, awards and much more.
            S.NoConference nameDateVenue
            1.DataHack Summit 20179 – 11 November, 2017MLR Convention Center, Whitefield, Bengaluru
            2.The Fifth Elephant 201727-28 July, 2017, Bengaluru Dyvasandra Industrial Layout    Mahadevapura, Whitefield,  Kaveri Nagar,
            3.NASSCOM Big Data & Analytics Summit 2018Jul 11, 2018 - July 12, 2018Taj Yeshwantpur, 2275, Tumkur Road, Yeshwanthpur, Bengaluru, India

            1. DataHack Summit 2017, Bengaluru, India

            • Conference City: Bengaluru, India
            • About: DataHack Summit aimed at highlighting the innovative work accomplished by scientists and researchers all across the globe and presenting it to the rest of the industry. The conference’s motto was “We talk data science, we breathe data science and we experience data science like never before.”
            • Event Date:  9 – 11 November, 2017
            • Venue: MLR Convention Center, Whitefield, Bengaluru
            • Days of Program: 3
            • Timings: 8:00 AM to 5:00 PM 
            • Purpose: DataHack Summit brought together an amazing opportunity to listen to over 30 speakers and discuss fresh developments in the arena of machine learning and data analytics
            • How many Speakers: Eight Speakers and Two Keynote Sessions 
            • Speakers & Profile: Dr. Kirk Borne, Principal Data Scientist - Booz Allen Hamilton | Dr Satnam Singh, Chief Data Scientist - Acalvio Technologies | Anand S, Founder & CEO - Gramener
            • Registration cost: INR 7000 - First Day Ticket | INR 10000 - Regular Ticket | INR - 13000 - Closing Day 
            • Who are the major sponsors: 
              • Intel
              • IBM
              • Praxis
              • Great 

              2. The Fifth Elephant 2017, Bengaluru, India

              • Conference City: Bengaluru
              • About: Hailed as the best conference on engineering, data science, and machine learning, the sixth edition of The Fifth Elephant brought a two-day nonstop station of architecture decisions, building data pipelines, data visualization and data in government. 
              • Event Date: 27-28 July, 2017, Bengaluru
              • Venue: Dyvasandra Industrial Layout Mahadevapura, Whitefield, Kaveri Nagar, Krishnarajapura
              • Days of Program: Two
              • Timings: 8:15 AM to 8:15 PM 
              • Purpose: The purpose of the conference was to facilitate interactions and conversations between experts and participants, covering various spheres of data, analytics, data privacy, and social policy. 
              • Speakers & Profile: 
                • Paul Meinshausen, Data Scientist in Residence at Montane Ventures
                • Bhargav Kowshik, Software Engineer at Mapbox, Bengaluru
                • Rakesh Dubbudu, Founder of Factly
                • Vimal Sharma, Apache Atlas Committer at Hortonworks
              • Registration cost: Free Entry 
              • Who were the major sponsors:  
                • Uber
                • COWRKS
                • Razorpay
                • Go JEK
                • MathWorks
                • Walmart
                • iMerit

                3. NASSCOM Big Data & Analytics Summit 2018, Bengaluru, India

                • Conference City: Bengaluru 
                • About: Set on the theme "Democratizing AI: From Disruption to Business as Usual" the conference brought together internationally acclaimed faces of the data science world for a fruitful discussion about the ongoing policies and areas of development in technology and big data management. 
                • Event Date: Jul 11, 2018 - Jul 12, 2018
                • Venue: Taj Yeshwantpur, 2275, Tumkur Road, Yeshwanthpur, Bengaluru, India
                • Days of Program: Two
                • Timing: 08:00 AM - 05:15 PM
                • Purpose: The conference focused on Supply Chain Application, Customer Experience and Operational Effectiveness, Embedding AI into business processes and Protecting Data in the AI age.
                • Speakers & Profile: 
                  • Piyush Chowhan, Arvind Fashions Limited, Vice President - IT /CIO
                  • Christina Clark, GE Power, Chief Data Officer
                  • Doug Jennings, Lowe’s Companies Inc., VP - Data Analytics & Customer Insights (DACI)
                • Registration cost: INR 14,500 (May Vary)
                • Who were the major sponsors: 
                  • Course5
                  • Lowe's
                  • XEXL 
                  • KELLTON TECH

                Data Scientist Jobs in Bangalore

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

                1. Getting started with programming
                2. Mathematics and Statistics
                3. Libraries
                4. Data visualization
                5. Data preprocessing
                6. Machine Learning and Deep Learning
                7. Natural Language processing
                8. Brushing up skills

                The 5 important steps to prepare for data scientist jobs are as follows:

                • Study: You must cover important topics theoretically with keywords and definitions. The following topics should be covered-
                  • Probability
                  • Statistics
                  • Statistical models
                  • Data Sets
                  • Machine Learning
                  • Artificial Intelligence
                • Meetups and conferences: Tech meetups and data science conferences provide you with an opportunity to connect and meet people with similar interests. This helps you build a strong professional network that will also help you keep updated and get referrals for your desired job.
                • Competitions: There are many competitions and hackathons being conducted online as well as offline. Participating in these will help you showcase your talent to a bigger audience. Apart from being a great opportunity for learning, it is also a chance to assess yourself and track your learning. 
                • Referral: Referral will help you get an interview slot easily. It’s important to keep good connections. Make sure to keep updating your LinkedIn profile.

                • Interview: Attend the interviews that excite you and make sure to appear for as many as you can. It will give you an idea of the kind of questions and the expectations and requirements of the companies.

                Data scientists are vital to companies. They take an enormous mass of unstructured and structured data points and use their formidable skills in math, statistics, and programming to clean, arrange and organize them. Then they apply all their analytic powers like industry knowledge, contextual understanding, skepticism of existing assumptions to provide hidden solutions to business challenges. Some of the responsibilities can be listed as follows:

                • Use statistical skills to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes.
                • Mine and analyze data from company databases to drive optimization and improvement of marketing techniques, product development, and business strategies.
                • Develop custom data models and algorithms to apply to data sets according to the needs of the company.
                • Develop algorithms and tools to monitor and analyze model performance and data accuracy.
                • Develop company A/B testing framework and test model quality and datasets.

                The average salary for a Data Scientist in Bangalore, Karnataka is Rs 902,866 per year. 

                The various steps in the career path of a Data Scientist in sequential order is given as below:

                • Executive: At this level, the following roles are offered
                  • CRM reports
                  • MIS Management Information System
                  • DQA Data Quality Assurance
                • Analyst: Data Analysts gather information from various sources and interpret patterns and trends and turns it into information which can offer ways to improve a business. The roles offered are as follows:
                  • Data Munging
                  • Product tech support
                  • Sales lead and CRM
                  • Data Visualization
                  • Advanced Statistics
                • Business Analyst: The main motive of a Business Analyst is to help businesses implement technology solutions in a cost-effective way by determining the requirements of a project or program, and communicating them clearly to stakeholders, facilitators, and partners. The various roles performed by a business analyst are as follows:
                  • Data Engineer
                  • Business processes
                  • Vendor coordination
                  • product/ project SDLC
                  • MDM & Logic
                • Data Architect: The responsibilities of Data Architect is to create database solutions, evaluating requirements, and prepare design reports.
                  • Data warehousing
                  • Data modeling
                  • Data cleaning
                  • ETL working
                  • Elastic working and functioning.
                • Chief Data Officer/ Data Scientist: The chief data officer is a senior executive responsible for the utilization and governance of data across the organization through data management, ensuring data quality and creating data strategy. The various roles include the following:
                  • Advanced predictive algorithm
                  • Data advanced algorithm
                  • NoSQL
                  • Big Data processing
                  • ETL Logic

                Below are the top professional organizations for data scientists in Bangalore – 

                • GeekHouse - AI, Machine Learning & Data Science - Bangalore
                • Start your Career in Data Science
                • Data Science & Internet of Things (IoT)
                • Bangalore Natural Language Processing Meetup
                • Bengaluru Data Science #ODSC

                Some of the other ways to network with data scientists to fill potential employees are as follows:

                • Attend Data science conference
                • Attend workshops
                • Follow influential data scientists
                • Go to local meet-ups
                • Use Online platform like LinkedIn

                There are several career options for a data scientist in Bangalore – 

                1. Data Scientist
                2. Data Administrator
                3. Data Architect
                4. Business Intelligence Manager
                5. Marketing Analyst
                6. Healthcare data analyst
                7. Data Analyst
                8. Business Analyst

                Bangalore has some of the biggest companies such as LinkedIn, Amazon, WyngCommerce, Vedantu, Michael Page, Citi, etc which offer jobs to Data Science professionals. The offer high salaries but also demand in-depth knowledge in the field.

                Here are  the key points, which the employers generally look for while hiring data scientists:

                • Education: A graduate degree in Computer Science, IT, statistics, etc. is a requirement by most of the companies. Having a master's or Ph.D. is a bonus. It is advised to have certification in the related field to boost up your profile.
                • Programming: Python and R is a preferred choice as a programming language. It is advised to have mastered either of these to get better opportunities.

                • Projects: Mini or major projects in data science provides you a practical approach to how things work in real time. Here is the list of 5 topics that you can take up for your project:
                  • Data Cleaning
                  • Exploratory Data Analysis
                  • Machine Learning
                  • Interactive Data Visualization
                  • Communication.

                Data Science with Python Bangalore

                Python allows to explore the basics of machine learning and makes it easy and effective. Machine learning is more about statistics, optimization, mathematical and probability. Some of the reasons why Python is considered as the most popular language to learn Data Science are as follows:

                • Python requires less coding than other programming languages to achieve the same result.
                • It has powerful statistical and numerical packages
                • It is an independent platform
                • It has the ability to easily integrate with the existing Infrastructure and can also solve the most difficult of problems.
                • The Python testing framework is a built-in, low-barrier-to-entry testing framework that encourages good test coverage.

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

                • R: Although it has a steep learning curve, it has various advantages.
                  • R provides a wide variety of statistical and graphical techniques.
                  • R runs on all the platforms.
                  • R is open source.
                • Python: Python is one of the most popular and widely used high-level languages for Data Science. Its advantages are as follows:
                  • Easy to learn and implement it.
                  • It has a big open-source community as well.
                  • It is used to handle big data.
                • SQL: SQL (Structured Query Language) which works upon relational databases.
                  • It is used to insert, update, delete, create, etc in a database.
                  • Efficient at updating, manipulating and querying data in relational databases.
                  • Permissions and access controls can be set up within the database for data security.
                • Julia: Julia is an open-source, scripting like a programming language that provides good support for interactive use. Some of the pros of Julia are as follows:
                  • It is easy to use having a level of syntax.
                  • It has a rich language of descriptive data types
                  • The standard library provides asynchronous I/O, logging, process control, a package manager, profiling, and more.
                  • It compiles to efficient native code for multiple platforms via LLVM
                • Scala: Scala is a multi-paradigm programming language designed to express common programming patterns in an elegant, concise, and type-safe way. Some of the features of Scala are as follows:
                  • Scala is object-oriented.
                  • Scala is functional.
                  • It is statically typed.
                  • It runs on the JVM.
                  • It can execute JAVA code.
                  • It can do concurrent and synchronize processing.

                Follow these steps to successfully install Python 3 on windows:

                • Download the installer from the downloads section of the official Python website.
                • simply run the installer by double-clicking on the downloaded file
                • Then just click Install Now.

                Note: You must ensure to check the box that says Add Python 3.x to PATH as shown to ensure that the interpreter will be placed in your execution path.


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

                1. You should install GCC first which can be obtained by downloading Xcode, the smaller Command Line Tools (must have an Apple account) or the even smaller OSX-GCC-Installer packageInstall.
                2. While OS X comes with a large number of Unix utilities, a package manager is a key component. 
                3. To install Homebrew, open Terminal or your favorite OS X terminal emulator and run

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

                1. Insert the Homebrew directory at the top of your PATH environment variable. For this, add the following line at the bottom of your ~/.profile file

                export PATH="/usr/local/opt/python/libexec/bin:$PATH"

                1. Install Python 3 by writing the following code

                $ brew install python

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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!

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                Data Science with Python Certification Course in Bangalore

                With a city life that is vibrant and fresh, Bangalore represents the new modern face of India. At the core of India?s booming IT industry, Bangalore is home to the headquarters of many global IT giants including Infosys and Wipro- so much so that it has earned itself the moniker of India?s Silicon Valley. The city has a rich history and has been ruled by a succession of South Indian dynasties, many of whose palaces and forts now nestle next to Bangalore?s starkly modern glass towers. Many would say Bangalore?s old world charm has now given way to haphazard unplanned development, congested city roads and rising pollution. But this does not take away from the mad rush for jobs in Bangalore?s progressive professional scene. All this makes Bangalore an ideal place to study and work in for those who are interested in IT. Professionals who wish to thrive in their career would find that they can do well here, with certifications such as Big Data and Hadoop 2.0 Developer, ITIL Foundation, PMP, Python 101, TOGAF 9.1, CEH and others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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