Data Science with Python Training in Bangalore, India

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.

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:

• • http://datasource.kapsarc.org/
  • https://kaggle.com/datasets
  • http://aws.amazon.com/datasets
  • http://linkeddata.org
  • http://reddit.com/r/datasets
  • http://thewebminer.com

• 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

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

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.

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:
  • Chiranjiv Roy Global- VP of Data Science at Nissan Motors
  • Vijaya Ghosh- Consumer Products Leader Analytics & Insights at TCS
  • Susan Mani- Analytics Delivery Leader at Analytic Edge 
  •  Sarita Digumarti- Co-Founder at Jigsaw Academy
  •  Medy Agami- Adjunct Professor at the University of Chicago
  •  Ashwini Raskar- Manager Software Development at IDeaS
  •  Mangesh Patankar- Developer Advocate at IBM 
  •  Rajesh Gudikoti- Developer Advocate at IBM
  •  Phani Mitra- VP Analytics & Strategy at Dr. Reddy’s
  • Prateep Basu- Co-Founder & Global Head - Strategy & Partnerships at SatSure
  •  Harini Srinivasan- Senior Technical Staff Member, The Weather Company, an IBM Business
  •  Vinodh Ramachandran- Associate Director at Lowe's India
  •  Srinath Jangam- Global Head of Advance Analytics at L&T Construction
  •  Anirudh Shah- Founder & CEO at 3LOQ Labs
  •  Krishnan Thyagarajan- President & COO at DataWeave
  • Amit Kurhekar- Senior Technology Manager at Procter & Gamble
  •  Siddhartha Roy Head- Analytics, AI & Machine Learning Practice at Thoucentric.
  •  Mayank Tewari- Data Science Consultant at ZS Associates
  •  Rajesh K Jeyapaul- Sr. Developer Architect at IBM
  •  Amit Gupta- Head, Strategy and New Revenue Development at HT Media
  •  Preetham Shanbhag- Executive Director & GM - FP&A Operations & Transformation at Honeywell Technology Solutions
  •  Prateek Agrawal- Director at Ivy Professional School
  • Tushita Chatterjee- Associate Vice President at Equifax Data & Analytics
  •  Dhruv Kumar- Vice President Analytics at ACT Fibernet
  •  Ajay Khetan- COO at Home First Finance Company
  •  Prakash Sundaram- Chief Strategy & Digital Innovation Officer at Fincare – small finance bank
  •  Hindol Basu- CEO at Actify Data Labs
  •  Amit Soni- Group Head Analytics in Piramal Enterprises
  • Vinit Gela- Data Science Associate Consultant at ZS Associates
  • Sagar Madgi- Data Science Manager at ZS Associates
• 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.

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

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.

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)"

4. 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"

5. Install Python 3 by writing the following code

$ brew install python