The document discusses the growing field of data science. It begins by defining data science and explaining how the rise of big data and the internet of things has led to an increasing demand for data scientists. It then examines the skills and qualifications needed for different types of data science roles, including data analysts, engineers, and research scientists. Finally, it provides resources for continuing to learn about data science.
Learn about the emerging field of big data and advanced quantitative models and how the Rady School's MS in Business Analytics program is designed to solve important business problems.
This is a presentation that I presented in the talk of "Woman in Data science" in Turin, 2018. This is a guide to help beginners to start their journey in Data science, it provided suggestion where to start, what to study, what are the best online & off-line resource & materials and how to put all the theory in practice. Enjoy your journey!
Introduction to Analytic fields. Data Analytics. What is Analytics. What it takes to be a Analyst, Different Profiles in Analytics fileds, Data science, data analytics, big data profiles, etc
Learn about the emerging field of big data and advanced quantitative models and how the Rady School's MS in Business Analytics program is designed to solve important business problems.
This is a presentation that I presented in the talk of "Woman in Data science" in Turin, 2018. This is a guide to help beginners to start their journey in Data science, it provided suggestion where to start, what to study, what are the best online & off-line resource & materials and how to put all the theory in practice. Enjoy your journey!
Introduction to Analytic fields. Data Analytics. What is Analytics. What it takes to be a Analyst, Different Profiles in Analytics fileds, Data science, data analytics, big data profiles, etc
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
Keynote - An overview on Big Data & Data Science - Dr Gregory Piatetsky-ShapiroData ScienceTech Institute
Data Science Tech Institute - Big Data and Data Science Conference around Dr Gregory Piatetsky-Shapiro.
Keynote - An overview on Big Data & Data Science Dr Gregory Piatetsky-Shapiro - KDnuggets.com Founder & Editor.
Paris May 23rd & Nice May 26th 2016 @ Data ScienceTech Institute (https://www.datasciencetech.institute/)
This Presentation gives an insight into what is big data, data analytics, difference between big data and data science.And also salary trends in big data analytics.
The objective of this project is to discuss the importance of Machine Learning in different sectors and how does it solve the problems in the Marketing Analytics field. We have discussed Marketing Segmentation, Advertisement, and Fraud detection in our project. We used different Machine Learning algorithms and used R and Python library to predict and solve these problems. After making models and running test data on those models we got following results:
• We trained a Decision tree and Random Forest classifier model which has 73% accuracy to predict whether a person will be a defaulter or not based on credit history, income, job type, dependents etc.
• We segmented the Social networking profiles based on the likes and dislikes of a person using K-Means Clustering.
• We made a predictive model of the messages a customer receives and determined whether a message will be a Spam or not a spam with an accuracy of 97%. We used Naïve Bayes classifier for this model.
A brief overview of the Rady School of Management at UC San Diego and how are developing ethical leaders for innovation-driven organizations. Find out if the Rady School is the MBA or Master of Finance program for you.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
Data Scientist: The Sexiest Job in the 21st CenturyLyn Fenex
Presentation from WUSS 2015:
“Data scientist” is often used as a blanket title to describe jobs that are drastically different. There are plenty of articles and discussions on the web about what data science is, what qualities define a data scientist, how to nurture them, and how you should position yourself to be a competitive applicant. There are far fewer resources out there about the steps to take in order to obtain the skills necessary to practice this elusive discipline. This presentation will explore a collection of freely accessible materials and content to jumpstart your understanding of the theory and tools of Data Science. We will also discuss some of the variable understandings that companies use to define the roles of their Data Scientists.
Evolution of Data Analytics: the past, the present and the futureVarun Nemmani
This paper delves into the topic of advanced analytics, the current industry demands to utilize and analyze huge/diverse amounts of data, how big data analytics is becoming a part of the decision making process and to anticipate trends. This paper takes the reader from Analytics era 1.0 to the current Analytics era 3.0; shows the future projections of big data analytics and also the current leaders of the Big Data Analytics market.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
Keynote - An overview on Big Data & Data Science - Dr Gregory Piatetsky-ShapiroData ScienceTech Institute
Data Science Tech Institute - Big Data and Data Science Conference around Dr Gregory Piatetsky-Shapiro.
Keynote - An overview on Big Data & Data Science Dr Gregory Piatetsky-Shapiro - KDnuggets.com Founder & Editor.
Paris May 23rd & Nice May 26th 2016 @ Data ScienceTech Institute (https://www.datasciencetech.institute/)
This Presentation gives an insight into what is big data, data analytics, difference between big data and data science.And also salary trends in big data analytics.
The objective of this project is to discuss the importance of Machine Learning in different sectors and how does it solve the problems in the Marketing Analytics field. We have discussed Marketing Segmentation, Advertisement, and Fraud detection in our project. We used different Machine Learning algorithms and used R and Python library to predict and solve these problems. After making models and running test data on those models we got following results:
• We trained a Decision tree and Random Forest classifier model which has 73% accuracy to predict whether a person will be a defaulter or not based on credit history, income, job type, dependents etc.
• We segmented the Social networking profiles based on the likes and dislikes of a person using K-Means Clustering.
• We made a predictive model of the messages a customer receives and determined whether a message will be a Spam or not a spam with an accuracy of 97%. We used Naïve Bayes classifier for this model.
A brief overview of the Rady School of Management at UC San Diego and how are developing ethical leaders for innovation-driven organizations. Find out if the Rady School is the MBA or Master of Finance program for you.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
Data Scientist: The Sexiest Job in the 21st CenturyLyn Fenex
Presentation from WUSS 2015:
“Data scientist” is often used as a blanket title to describe jobs that are drastically different. There are plenty of articles and discussions on the web about what data science is, what qualities define a data scientist, how to nurture them, and how you should position yourself to be a competitive applicant. There are far fewer resources out there about the steps to take in order to obtain the skills necessary to practice this elusive discipline. This presentation will explore a collection of freely accessible materials and content to jumpstart your understanding of the theory and tools of Data Science. We will also discuss some of the variable understandings that companies use to define the roles of their Data Scientists.
Evolution of Data Analytics: the past, the present and the futureVarun Nemmani
This paper delves into the topic of advanced analytics, the current industry demands to utilize and analyze huge/diverse amounts of data, how big data analytics is becoming a part of the decision making process and to anticipate trends. This paper takes the reader from Analytics era 1.0 to the current Analytics era 3.0; shows the future projections of big data analytics and also the current leaders of the Big Data Analytics market.
A look at the evolution of analytics and its revolutionary potential to transform ordinary businesses, power new business models, enable innovation, and deliver greater value. http://www2.deloitte.com/us/en/pages/deloitte-analytics/articles/analytics-trends.html
Data lineage is a regulatory and internal requirement with potential to deliver significant operational and business benefits, but financial institutions can find it difficult to implement and complex to maintain as systems and regulatory requirements themselves, change quickly. The importance of understanding where the true source of the data is coming from, where the data flows to and what has changed cannot be overstated. The webinar defines data lineage and discuss implementation through the eyes of those that have implemented and sustained successful lineage solutions with significant benefits.
Listen to the webinar to find out about:
- Data management for data lineage
- Winning buy-in for projects
- Best practice implementation
- Operational and business benefits
- Expert practitioner advice
As 2017 begins, we are seeing big data and data science communities engage with new tools that specifically cater to data scientists and data engineers who aren’t necessarily experts in these techniques. Given rapid technological advances, the question for companies now is how to integrate new data science capabilities into their operations and strategies—and position themselves in a world where analytics can upend entire industries. Leading companies are using their data science capabilities not only to improve their core operations but also to launch entirely new business models.
Data, Insight, & Action - University of Utah IS 6482 Data Mining Jan 2015Richard Sgro
A presentation I gave at the University of Utah about the role of data in companies of various stages. Also featured a brief discussion on careers working with data.
Tracxn Big Data Analytics Landscape Report, June 2016Tracxn
New Enterprise Associates, Andreessen Horowitz, Accel Partners, Intel Capital and Khosla Ventures are the top 5 investors in big data analytics, with over 10 investments each.
Exploring the impact and evolution of Advanced Analytics Tools.pdfStats Statswork
The impact and evolution of advanced analytics tools have transformed how businesses operate, offering unprecedented insights and decision-making capabilities. Statstwork has been at the forefront of this evolution, providing cutting-edge solutions that leverage big data, machine learning, and AI. These tools enable companies to analyze vast amounts of data in real-time, identify trends, and predict future outcomes with high accuracy. As a result, businesses can optimize their operations, enhance customer experiences, and drive innovation. The continuous advancement of these tools promises even greater efficiencies and opportunities, making them indispensable in the modern data-driven landscape.
For more information contact:
https://www.statswork.com
& https://www.statswork.com/contact-us/
Contact our Experts:
Our Email id: info@statswork.com
Contact No: +91 8754467066
Exploring the impact and evolution of Advanced Analytics Tools.pdfStats Statswork
The impact and evolution of advanced analytics tools have transformed how businesses operate, offering unprecedented insights and decision-making capabilities. Statstwork has been at the forefront of this evolution, providing cutting-edge solutions that leverage big data, machine learning, and AI. These tools enable companies to analyze vast amounts of data in real-time, identify trends, and predict future outcomes with high accuracy. As a result, businesses can optimize their operations, enhance customer experiences, and drive innovation. The continuous advancement of these tools promises even greater efficiencies and opportunities, making them indispensable in the modern data-driven landscape.
For more information contact:
https://www.statswork.com
& https://www.statswork.com/contact-us/
Contact our Experts:
Our Email id: info@statswork.com
Contact No: +91 8754467066
Why is big data all the rage? What is this "data science" that people are talking about? Why do I care — as a customer, and as someone who works at a company generating data? In this talk, I present the case for models, and how we can use data science to create and use models of our customers and the society around us.
Similar to Data Scientist: The Sexiest Job in the 21st Century (20)
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
2. WUSS 2015
Experis | Friday, February 26, 2016 2
Our Time Today
– What is Data Science?
– Who needs a Data Scientist?
– What makes a Data Scientist?
– What kind of Data Scientist are
you?
Specialized
Talent and
Solutions
New
Work
Models
Shifts in
Business
4. WUSS 2015
Experis | Friday, February 26, 2016 4
What is Data Science, anyway?
5. WUSS 2015
Experis | Friday, February 26, 2016 5
Hal Varian, the chief economist at Google, is known to have
said, “The sexy job in the next 10 years will be statisticians.
People think I’m joking, but who would’ve guessed that
computer engineers would’ve been the sexy job of the 1990s?”
The Hot Job of the Decade
Source: HBR
6. WUSS 2015
Experis | Friday, February 26, 2016 6
The internet population now has over 2.1 billion people, and with every
website browsed, status shared, and photo uploaded, we leave a digital trail
that continually grows the hulking mass of big data.
Every minute, on average,
• YouTube users upload 48 hours of video
• Facebook users share 684,478 pieces of content,
• Instagram users share 3,600 new photos, and
• Tumblr sees 27,778 new posts published.
A perspective on how Big is Data
Source: That Conference, 2015
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Experis | Friday, February 26, 2016 7
“By 2015, 4.4 million IT jobs globally will be created to support big
data, generating 1.9 million IT jobs in the United States,” said Peter
Sondergaard, senior vice president at Gartner and global head of
Research. “In addition, every big data-related role in the U.S. will
create employment for three people outside of IT, so over the next
four years a total of 6 million jobs in the U.S. will be generated by
the information economy.“
Source:
Gartner Symposium/ITxpo 2012
The Information Economy
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Experis | Friday, February 26, 2016 16
A big data scientist understands how to integrate multiple systems and
data sets.
They need to be able to link and mash up distinctive data sets to
discover new insights.
This often requires connecting different types of data sets in different
forms as well as being able to work with potentially incomplete data
sources and cleaning data sets to be able to use them.
Sound familiar?
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Experis | Friday, February 26, 2016 17
8 Data Skills to Get You Hired
• Basic Tools
• Basic Statistics
• Machine Learning
• Multivariable Calculus and Linear Algebra
• Data Munging
• Data Visualization & Communication
• Software Engineering
Source: Udacity
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Experis | Friday, February 26, 2016 18
Most in-demand data skills
Source: WANTED Analytics, 2014
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Experis | Friday, February 26, 2016 20
Source: Udacity
4 Types of Data
Science Roles
21. WUSS 2015
Experis | Friday, February 26, 2016 21
All joking aside, there are in fact some companies where being a data
scientist is synonymous with being a data analyst.
Your job might consist of tasks like pulling data out of MySQL
databases, becoming a master at Excel pivot tables, and producing
basic data visualizations (e.g., line and bar charts).
You may on occasion analyze the results of an A/B test or take the lead
on your company’s Google Analytics account.
.
A Data Scientist is a Data Analyst Who Lives in San Francisco:
22. WUSS 2015
Experis | Friday, February 26, 2016 22
You’ll see job postings listed under both “Data Scientist” and “Data
Engineer” for this type of position.
Since you’d be (one of) the first data hires, there are likely many low-
hanging fruit, making it less important that you’re a statistics or machine
learning expert.
A data scientist with a software engineering background might excel at
a company like this, where it’s more important that a data scientist make
meaningful data-like contributions to the production code and provide
basic insights and analyses.
Please Wrangle Our Data!
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Experis | Friday, February 26, 2016 23
There are a number of companies for whom their data (or
their data analysis platform) is their product. In this case, the
data analysis or machine learning going on can be pretty
intense.
This is probably the ideal situation for someone who has a
formal mathematics, statistics, or physics background and is
hoping to continue down a more academic path.
Data Scientists in this setting likely focus more on producing
great data-driven products than they do answering operational
questions for the company.
We Are Data. Data Is Us:
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Experis | Friday, February 26, 2016 24
A lot of companies fall into this bucket. In this type of role, you’re joining
an established team of other data scientists.
The company you’re interviewing for cares about data but probably isn’t a
data company. It’s equally important that you can perform analysis, touch
production code, visualize data, etc.
Generally, these companies are either looking for generalists or they’re
looking to fill a specific niche where they feel their team is lacking, such
as data visualization or machine learning.
Reasonably Sized Non-Data Companies Who Are Data-Driven:
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Experis | Friday, February 26, 2016 25
Business Brain
• Concentrate on developing a “business brain” in addition to those
hard data skills.
• Data insights are useless without the foundational knowledge of the
business to which the data belongs.
• Keep your eyes and ears open to absorb as much understanding of
how a business and strategy works.
• Develop presentations to advise senior management in clear language
about the implications of their work for the organization.
• Develop ability to create examples, prototypes, demonstrations to help
management better understand the work.
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Experis | Friday, February 26, 2016 26
The Changing Role of Analysts
• Statistics done by non-Statisticians,
• The growth of Statistics into new areas such as healthcare and financial
applications,
• Greater expectations by management for statisticians to “be responsive
and vital to today's business needs and to be able to prove their
contributions quantitatively,”
• The requirements of analyses to be timely as well as appropriate,
• The need to work with immense databases, and
• Adapting to new forms of communication. (Hahn & Hoerl, 1998)
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Experis | Friday, February 26, 2016 28
Resources for ongoing learning
29. WUSS 2015
Experis | Friday, February 26, 2016 29
As Peter Sondergaard, global head of research at Gartner, said in a
2012 statement,
The most valued data analysts of tomorrow will be able not only to
derive insights from existing data sets, but also to tell the quantitative
future:
“Dark data is the data being collected, but going unused despite its
value. Leading organizations of the future will be distinguished by the
quality of their predictive algorithms. This is the CIO challenge, and
opportunity.”
In conclusion
The investment made in data collection demands putting the data to good use to drive business results forward.
Big data and analytics are all around these days. IBM projects that every day we generate 2.5 quintillion bytes of data. This means that 90% of the data in the world has been created in the last two years.
In 2013, Gartner projected that by 2015, 85% of Fortune 500 organizations would be unable to exploit big data for competitive advantage and that 6M jobs would be created as a result of the data explosion.
Couple of key points:
Using data for predictive analysis
30+% believe that their demand will exceed the supply of available talent
Also- traditional BI professionals may not have the profile required to impact these new requirements. Like SAS says- looking through the windshield instead of the rear view mirror.
According to this graph from Forbes, the greatest preponderance of Data Scientists are working in the services information industry-
Search engines (Google, Microsoft), social networks (Twitter, Facebook, LinkedIn), financial institutions, Amazon, Apple, eBay, the health care industry, engineering companies (Boeing, Intel, Oil industry), retail analytics, mobile analytics, marketing agencies, data science vendors (for instance, Pivotal, Teradata, Tableau, SAS, Alpine Labs), environment, utilities government and defense routinely hire data scientists, though the job title is sometimes different.
Traditional companies (manufacturing) tend to call them operations research analysts.
This article cited that there is very little difference in result between 2013 and 2015, except that there appears for be more growth of hiring in smaller companies.
But these companies corroborate the previous graph; platform and software developers, as well as service/consulting companies are the largest employers of this skills set.
In this graphic, It is this outer ring of skills that are fundamental in becoming a data scientist.
The skills in the inner part of the diagram are skills that most people will have some experience in one or more of them.
The other skills can be developed and learned over time, all depending on the type of person you are.
When it comes to being a data scientist it might be fair to say you are a ‘A jack of all trades and a master of some’.
This graph outlines some of the characteristics that EMC thinks makes a Data Scientist- notice that none of these are technical skills
Many resources out there may lead you to believe that becoming a data scientist requires comprehensive mastery of a number of fields, such as software development, data munging, databases, statistics, machine learning and data visualization.
Don’t worry. You don’t need to learn a lifetime’s worth of data-related information and skills as quickly as possible. Instead, learn to read data science job descriptions closely. This will enable you to apply to jobs for which you already have necessary skills, or develop specific data skill sets to match the jobs you want.
The big data scientist needs to be able to program, preferably in different programming languages such as Python, R, Java, Ruby, Clojure, Matlab, Pig or SQL.
You need to have an understanding of Hadoop, Hive and/or MapReduce.
Many resources out there may lead you to believe that becoming a data scientist requires comprehensive mastery of a number of fields, such as software development, data munging, databases, statistics, machine learning and data visualization.
Don’t worry. You don’t need to learn a lifetime’s worth of data-related information and skills as quickly as possible. Instead, learn to read data science job descriptions closely. This will enable you to apply to jobs for which you already have necessary skills, or develop specific data skill sets to match the jobs you want.
Hopefully this gives you a sense of just how broad the title ‘data scientist’ is. Each of the four company ‘personalities’ above are seeking different skillsets, expertise, and experience levels. Despite that, all of these job postings would likely say “Data Scientist,” so look closely at the job description for a sense of what kind of team you’ll join and what skills to develop.
A company like this is a great place for an aspiring data scientist to learn the ropes. Once you have a handle on your day-to-day responsibilities, a company like this can be a great environment to try new things and expand your skillset
It seems like a number of companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they’re looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis.
There will be less guidance and you may face a greater risk of flopping or stagnating.
Companies like CoreLogic, Lexus Nexus, RAND Corporation make their business out of identifying trends
Companies that fall into this group could be consumer-facing companies with massive amounts of data or companies that are offering a data-based service.
Some of the more important skills when interviewing at these firms are familiarity with tools designed for ‘big data’ (e.g., Hive or Pig) and experience with messy, ‘real-life’ datasets.
Being able to advice senior management in clear language about the implications of their work for the organization;
Having an, at least basic, understanding of how a business and strategy works;
Being able to create examples, prototypes, demonstrations to help management better understand the work;
[Use this slide to summarize what you know about the client’s current business challenges]