ACET EXAM MATERIAL

Starting reference link to : https://www.quora.com/How-should-I-prepare-for-the-ACET-Actuarial-Common-Entrance-Test-of-India

given below: https://www.learnpick.in/exams/acet-exam/important-links



You tube link : Vamsidhar ambatipudi sir classes in youtube  https://www.quora.com/How-do-I-prepare-for-maths-for-acet


http://insurology.in/2016/07/26/acet-reference-books-pdf-online-iai-reference/



secondary reference material : https://www.udemy.com/actuarial-mathematics/?couponCode=QUORA1


Mock Papers : 


https://stepupanalytics.com/acet-preparation-decoded/

Coaching in Delhi : https://futuretrack.org/ and online version : https://onlineactuarial.futuretrack.org/ (they provide training in ACET)

Coaching in Mumbai and Pune : http://actuaries101.blogspot.com/

Coaching in New Delhi & Kolkata : https://www.souravsirclasses.com/product-page/study-materials-for-actuarial-science-acet-examination-2017-december-entra


Books to be considered

  • TOMATO (Test Of Mathematics at Ten plus Two level published by ISI)
  • How to Prepare for Verbal Ability and Reading Comprehension for CAT by Arun Sharma and Meenakshi Upadhyay, Mc Graw Hill
  • How to prepare for Quantitative Aptitude for the CAT Editor: Arun Sharma
  • Quantitative Aptitude for Competitive Examinations Editor: Abhijit Guha
  • The Pearson and Guide to Verbal Ability and Logical Reasoning for the CAT Editor: Nishit K. Sinha
  • Quantitative Aptitude for MBA Entrance Examinations Editor: R.S. Aggarwal
  • High School English Grammar and Composition- Wren and Martin
  • Word Power Made Easy Editor: Norman Lewis
  • Problem Solving Strategies Editor: Arthur Engel
  • Challenge and Thrill of Pre-College Mathematics by New Age International Publishers
  • Challenging Mathematical Problems with Elementary Solutions Editors Volume I and II: A.M. Yaglom and I.M. Yaglom
  • An Excursion in Mathematics Editors: M. R. Modak, S. A. Katre, V. V. Acharya, V. M. Sholapurkar
  • Problem Primer for the Olympiad Editors: C R Pranesachar, B J Venkatachala, C S Yogananda
  • Trishna’s Data Interpretation and Logical Reasoning for the CAT and other MBA Entrance Examinations
  • ACTED Study Material – FAC and STATS PACK.


FAC and STATS PACK Book

Very Expensive course on actuaries

http://sps.columbia.edu/certificates/actuarial-science-certificate/tuition-and-financing

http://sps.columbia.edu/actuarial-science

https://www.collegechoice.net/rankings/best-actuarial-science-degrees/

https://www.collegevaluesonline.com/rankings/best-value-actuarial-science-programs/


http://www.businessresearchguide.com/degrees/best/careers-becoming/actuary/

https://www.uis.edu/math/

https://thebestschools.org/rankings/best-online-bachelor-in-mathematics-degree-programs/

https://www.appliedmathonline.uw.edu/program-details/courses-curriculum/

https://www.appliedmathonline.uw.edu/

https://math.washington.edu/campus-resources

https://math.washington.edu/courses-related-actuarial-examinations

http://www.concordia.ca/artsci/math-stats/programs.html

https://uwaterloo.ca/statistics-and-actuarial-science/

https://www.statistics.utoronto.ca/

http://depts.washington.edu/compfin/cfrm-ms/

https://amath.washington.edu/master-science-applied-mathematics


http://depts.washington.edu/compfin/non-degree/certs/qfcf/




Aryng Analytics Aptitude Test

Enter the following in the certification section on LinkedIn by clicking the button below
Certification name
Analytical Aptitude Assessment
Certification authority
Aryng
License number
JHLOufal721553787200
Time period - From
28-03-2019
Time period - To
Leave blank and toggle the checkbox for This certification does not expire
Certification URL

https://aryng.com/analytical-test-certificate/?cl=JHLOufal721553787200



https://aryng.com/aryngs-analytical-aptitude-assessment/

Good Analytics Articles on Forbes

https://hbr.org/2010/07/the-execution-trap

“A mediocre strategy well executed is better than a great strategy poorly executed.”

Balanced scorecard can help senior managers systematically link current actions with tomorrow’s goals, focusing on that place where, in the words of the authors, “the rubber meets the sky".

Core to data science is the ability to connect a problem, or market, to data. Even today, many practicing data scientist are required to be "full stack": problem definition and research design; statistics and applied mathematics training; software engineering experience; and, product articulation and business impact. Having one person, or even a small team, being responsible for all of these operational components across a whole organization is unsustainable. As such, these roles need to begin being broken down into constituent parts within organizations.

Best advice for someone to wants to go into data science?

The most successful data scientists I know are those driven by a curiosity for solving problems through data. To that end, I recommend starting by thinking about what kinds of problems they find most interesting. From there, go out and try to find some data in the problem area and begin experimenting. Run some basic summary statistics on the data, plot some basic graphs, or even build a simple model. This is also a great way to learn the relevant tools and methods, and the result of which can become a nice asset in a person's portfolio. This also, typically, leads people to ask a lot of new questions, which creates a virtuous feedback loop of learning and community discovery.

On average, a 1 percent price increase translates into an 8.7 percent increase in operating profits (assuming no loss of volume, of course). 

customer analytics dominate big data use in sales and marketing departments, supporting the four key strategies of increasing customer acquisition, reducing customer churn, increasing revenue per customer and improving existing products. 


Big Data Use Cases:

48% Customer Analytics

21% Operational Analytics

12% Fraud & Compliance

10% New Product & Service Compliance

10% Enterprise Data Warehouse Optimization

18 Best Analytics Tools Every Business Manager Should Know

Business experiments: Business experiments, experimental design and AB testing are all techniques for testing the validity of something – be that a strategic hypothesis, new product packaging or a marketing approach. It is basically about trying something in one part of the organization and then comparing it with another where the changes were not made (used as a control group). It’s useful if you have two or more options to decide between.

Visual analytics: Data can be analyzed in different ways and the simplest way is to create a visual or graph and look at it to spot patterns. This is an integrated approach that combines data analysis with data visualization and human interaction. It is especially useful when you are trying to make sense of a huge volume of data.

Correlation analysis: This is a statistical technique that allows you to determine whether there is a relationship between two separate variables and how strong that relationship may be. It is most useful when you ‘know’ or suspect that there is a relationship between two variables and you would like to test your assumption.

Regression analysis: Regression analysis is a statistical tool for investigating the relationship between variables; for example, is there a causal relationship between price and product demand? Use it if you believe that one variable is affecting another and you want to establish whether your hypothesis is true.

Scenario analysis: Scenario analysis, also known as horizon analysis or total return analysis, is an analytic process that allows you to analyze a variety of possible future events or scenarios by considering alternative possible outcomes. Use it when you are unsure which decision to take or which course of action to pursue.

Forecasting/time series analysis: Time series data is data that is collected at uniformly spaced intervals. Time series analysis explores this data to extract meaningful statistics or data characteristics. Use it when you want to assess changes over time or predict future events based on what has happened in the past.

Data mining: This is an analytic process designed to explore data, usually very large business-related data sets – also known as ‘big data’ – looking for commercially relevant insights, patterns or relationships between variables that can improve performance. It is therefore useful when you have large data sets that you need to extract insights from.

Text analytics: Also known as text mining, text analytics is a process of extracting value from large quantities of unstructured text data. You can use it in a number of ways, including information retrieval, pattern recognition, tagging and annotation, information extraction, sentiment assessment and predictive analytics.

Sentiment analysis: Sentiment analysis, also known as opinion mining, seeks to extract subjective opinion or sentiment from text, video or audio data. The basic aim is to determine the attitude of an individual or group regarding a particular topic or overall context. Use it when you want to understand stakeholder opinion.

Image analytics: Image analytics is the process of extracting information, meaning and insights from images such as photographs, medical images or graphics. As a process it relies heavily on pattern recognition, digital geometry and signal processing. Image analytics can be used in a number of ways, such as facial recognition for security purposes.

Video analytics: Video analytics is the process of extracting information, meaning and insights from video footage. It includes everything that image analytics can do plus it can also measure and track behavior. You could use it if you wanted to know more about who is visiting your store or premises and what they are doing when they get there.

Voice analytics: Voice analytics, also known as speech analytics, is the process of extracting information from audio recordings of conversations. This form of analytics can analyze the topics or actual words and phrases being used, as well as the emotional content of the conversation. You could use voice analytics in a call center to help identify recurring customer complaints or technical issues.

Monte Carlo Simulation: The Monte Carlo Simulation is a mathematical problem-solving and risk-assessment technique that approximates the probability of certain outcomes, and the risk of certain outcomes, using computerized simulations of random variables. It is useful if you want to better understand the implications and ramifications of a particular course of action or decision.

Linear programming: Also known as linear optimization, this is a method of identifying the best outcome based on a set of constraints using a linear mathematical model. It allows you to solve problems involving minimizing and maximizing conditions, such as how to maximize profit while minimizing costs. It’s useful if you have a number of constraints such as time, raw materials, etc. and you wanted to know the best combination or where to direct your resources for maximum profit.

Cohort analysis: This is a subset of behavioral analytics, which allows you to study the behavior of a group over time. It is especially useful if you want to know more about the behavior of a group of stakeholders, such as customers or employees.

Factor analysis: This is the collective name given to a group of statistical techniques that are used primarily for data reduction and structure detection. It can reduce the number of variables within data to help make it more useful. Use it if you need to analyze and understand more about the interrelationships among a large number of variables.

Neural network analysis: A neural network is a computer program modeled on the human brain, which can process a huge amount of information and identify patterns in a similar way that we do. Neural network analysis is therefore the process of analyzing the mathematical modeling that makes up a neural network. This technique is particularly useful if you have a large amount of data.

Meta analytics/literature analysis: Meta analysis is the term that describes the synthesis of previous studies in an area in the hope of identifying patterns, trends or interesting relationships among the pre-existing literature and study results. Essentially, it is the study of previous studies. It is useful whenever you want to obtain relevant insights without conducting any studies yourself.

In Resume Building for data science - Communication and prioritization is important so that interviewer is aware that we are knowing of this fact


6 key analytics skills used by successful analyst/data scientist are:

  1. DTD framework: Understanding and hands-on experience of the basic “Data to Decisions” framework
  2. SQL skills: Ability to pull data from multiple sources and collate: experience in writing SQL queries and exposure to tools like Teradata, Oracle etc. Some understanding of Big Data tools using Hadoop is also helpful.
  3. Basic “applied” stat techniques a.k.a. Business Analytics:Hands-on experience with basic statistical techniques: Profiling, Correlation analysis, Trend analysis, Sizing/Estimation, Segmentation (RFM, product migration etc.). If you are in a consumer business, this list would include hands-on comfort in A/B Testing (also called Design of Experiments)
  4. Working effectively with business side: Ability to work effectively with stakeholders by building alignment, effective communication and influencing
  5. Advanced “applied” stat techniques (hands-on) a.k.a. Predictive Analytics and machine learning: Hands-on comfort with advanced techniques: Time Series, Predictive Analytics – Regression and Decision Tree, Segmentation (K-means clustering), machine learning - neural networks and Text Analytics (optional)
  6. Stat Tools: Experience with one or more statistical tools like Python, R, SAS, SPSS, Knime or others.

 4 key data science skills needed by business professionals are:

  1. DTD framework: Understanding and hands-on experience of the basic “Data to Decisions” framework.
  2. Basic “applied” stat techniques: Hands-on experience with business analytics: Profiling, Correlation analysis, Trend analysis, Sizing/Estimation, Basic Segmentation and basics of A/B Testing (also called Design of Experiments)
  3. Working effectively with analysts: Ability to work effectively with Data Scientists/Analyst
  4. Advanced “applied” stat techniques (intro): High-level understanding of predictive analytics: Time Series, Predictive Analytics – Regression and Decision Tree, Segmentation, Neural Networks.


Key Attribute of an Actionable Insight:

1. Alignment

When an insight is closely tied to your key business goals and strategic initiatives, it’s more likely to drive action. If you don’t know how to react to a particular metric when it significantly increases or decreases, you might be looking at an unnecessary vanity metric. Insights based on key performance indicators (KPIs) and other key metrics inherently engender a sense of urgency that other data won’t. It’s easier to interpret and convert strategically-aligned insights into tactical responses because they often relate directly to the levers in your business that you control, influence or are focused on.

2. Context

It is hard to move forward on an insight if you are lacking ample background to appreciate why it’s important or unique. We often need to have a comparison or benchmark to give data proper context. For example, if your company generated 1,400 leads this week your reaction to this result could change entirely with a dash of context. If you know your marketing team typically generates 1,250 leads each week you might do nothing. However, if your company just sponsored and exhibited at your industry’s major convention last week, you may be wondering why it didn’t translate into significantly more leads. Without accompanying context, an insight can end up raising more questions than action. Having ample supporting details ensures the insight results in action and not unwarranted skepticism and objections.

3. Relevance

A single insight can be both a strong signal for one person and just more noise for another. There’s a level of subjectivity when it comes to the relevance of insights. In order to be relevant, an insight needs to be delivered to the right person at the right time in the right setting. If insights aren’t routed to the right decision makers, they will not receive the attention they deserve. If insights aren’t timely, they might be too stale for stakeholders to act on. If insights are trapped in an analytics tool that managers never access or delivered to devices they use infrequently, the insights may never reach the intended audience.

4. Specificity

The more specific and complete the insight is, the more likely it can be acted on. Sometimes insights based on KPIs and other high-level metrics can highlight interesting anomalies but lack sufficient detail to drive immediate action. For example, knowing your revenue is up 35% this month may be a cause to celebrate, but party planning might be premature without deeper insights. You might discover a massive fraudulent order messed up your online revenue numbers or a big customer win was based on promised product functionality that won’t exist for the foreseeable future. If an insight doesn’t adequately help to explain why something occurred, it’s not yet actionable. Deeper probing may be required before it’s ready for primetime.

5. Novelty

With so much competing data and information to digest, novel insights will have an advantage over more familiar insights. The first time your company spots a particular pattern will be more interesting and compelling than the tenth time, especially if you feel you already have a good handle on what’s driving the behavior. Curiosity can drive people to test or verify an unusual or unexpected finding in the hope that it sheds new light on a key subject area. This criterion speaks more to human nature than to how valuable an insight actually is. We become numb to certain insights if we feel as though they reinforce rather than challenge or evolve our current knowledge and beliefs.

6. Clarity

If people don’t clearly understand an insight, why it’s important and how it can help them—the insight will be overlooked and forgotten. Communicating insights effectively is important to their adoption and fruition. The right data visualizations and messaging can help explain insights so they are more easily understood and correctly interpreted. However, poor communication can cause the signal to be lost in the noise. A clearly communicated insight creates a strong signal that is hard to miss or ignore, and it prepares a pathway for action to occur.

With these six criteria you can weigh how “actionable” the insights are that you receive from your analytics and business intelligence tools. Regardless of the source of the insights—humans or machines—the more they line up with these attributes, the more actionable they will be for your business. Strategically-aligned tops random; relevant beats extraneous; novel trumps familiar—you get the idea.

Harvesting more insights from your data can yield tremendous returns for your company. However, as author Richard Bach said, “Any powerful idea is absolutely fascinating and absolutely useless until we choose to use it.” While the increased actionability of an insight doesn’t guarantee its adoption or application, it should motivate more individuals within your company to think more deeply about the data and encourage them to act on a more consistent basis. Make sure your hard-earned insights are as actionable as possible so they’re primed to drive value for your organization.


https://www.forbes.com/sites/louiscolumbus/2018/06/08/the-state-of-business-intelligence-2018/#7f7650297828

https://www.forbes.com/sites/metabrown/2016/10/31/5-big-data-and-analytics-learning-resources-that-most-people-miss-but-shouldnt/#7ec64af7ecd5

https://www.forbes.com/sites/metabrown/2016/05/26/choosing-data-analytics-training-thats-worth-the-investment/#cc671a555ba9


http://www.aprapros.com/#

https://www.smartdatacollective.com/

https://www.smartdatacollective.com/why-data-analytics-insurance-industry-is-major-game-changer/


https://www.smartdatacollective.com/5-brands-using-big-data-to-brilliantly-disrupt-tech/


https://gotrg.com/

https://junkcharts.typepad.com/numbersruleyourworld/


https://fivethirtyeight.com/


http://www.dataminingblog.com/

https://junkcharts.typepad.com/junk_charts/

https://junkcharts.typepad.com/numbersruleyourworld/

http://abbottanalytics.blogspot.com/

http://www.dataminingblog.com/

https://datamakesworld.com/

https://datamakesworld.com/2013/05/16/closing-the-loop-in-customer-experience-management-when-it-doesnt-work/

https://www.kdnuggets.com/websites/blogs.html


https://mathbabe.org/

https://mathbabe.org/2012/12/20/nate-silver-confuses-cause-and-effect-ends-up-defending-corruption/

https://statistically-funny.blogspot.com/

http://www.metabrown.com


Good Sources of Data : https://www.forbes.com/sites/bernardmarr/2016/02/12/big-data-35-brilliant-and-free-data-sources-for-2016/#6c28fb45b54d


links to study for actuary

https://www.google.co.in/search?q=best+books+for+actuarial&oq=best+books+for+actuarial+&aqs=chrome..69i57.15329j0j7&sourceid=chrome&ie=UTF-8


https://www.wallstreetmojo.com/top-best-actuaries-books/


https://www.google.com/search?q=great+books+for+soa+and+cas+actuary+certification&rlz=1C1GCEA_enIN840IN840&oq=great+books+for+soa+and+cas+actuary+certification&aqs=chrome..69i57.12363j0j7&sourceid=chrome&ie=UTF-8


https://www.quora.com/What-are-some-good-books-for-someone-interested-in-becoming-an-actuary


https://www.soa.org/books/


https://www.quora.com/What-are-some-books-for-actuarial-science


https://www.quora.com/What-book-should-I-refer-to-to-become-an-actuary


https://www.quora.com/What-are-some-good-Statistics-books-for-an-Actuarial-undergraduate


https://www.quora.com/Whats-a-better-profession-to-follow-for-someone-interested-in-mathematics-research-or-becoming-an-Actuary


https://etchedactuarial.com/tips-and-advice-thank-page/


https://www.google.com/search?rlz=1C1GCEA_enIN840IN840&q=What+is+the+difference+between+SOA+and+CAS%3F&sa=X&ved=2ahUKEwip5pe29YPhAhWo7HMBHaI6AMQQzmd6BAgFEAo


https://www.actuarialninja.com/actuarial-exams/choosing-between-soa-and-cas/


https://etchedactuarial.com/how-to-study-exam-p/


https://www.google.com/search?rlz=1C1GCEA_enIN840IN840&q=How+do+I+study+for+my+first+actuary+exam%3F&sa=X&ved=2ahUKEwip5pe29YPhAhWo7HMBHaI6AMQQzmd6BAgFEBM


https://www.quora.com/Is-actuarial-science-difficult


http://www.pstat.ucsb.edu/instruction/actuary/study.html


https://www.soa.org/library/newsletters/the-future-actuary/2007/summer/how-to-prepare.aspx


https://www.soa.org/library/newsletters/the-future-actuary/2007/summer/how-to-prepare.aspx


https://www.soa.org/library/newsletters/the-future-actuary/2007/summer/how-to-prepare.aspx


https://www.beanactuary.org/exams/


https://www.amazon.in/Top-Secrets-Passing-Actuary-Exams-ebook/dp/B016YOCMUQ


https://www.amazon.in/Top-Secrets-Passing-Actuary-Exams-ebook/dp/B016YOCMUQ


https://www.theinfiniteactuary.com/


https://www.actexmadriver.com/


https://www.actexmadriver.com/asmstudymanuals.aspx


https://www.google.com/search?q=tia+vs+actex+vs+asm&rlz=1C1GCEA_enIN840IN840&oq=tia+vs+actex+vs+asm&aqs=chrome..69i57.5927j0j7&sourceid=chrome&ie=UTF-8


http://www.actuarialoutpost.com/actuarial_discussion_forum/showthread.php?t=193978&page=2

https://www.actuary.com/actuarial-discussion-forum/archive/index.php/t-21466.html


https://etchedactuarial.com/best-study-manual-for-exam-p/


https://www.soa.org/education/exam-req/edu-exam-p-detail.aspx


https://www.actuarialbookstore.com/samples/ASM_1P-ASM-18SSMP_SAMPLE_10-23-17.pdf


https://www.actuarialbookstore.com/samples/1P-ACT-18SSM_SAMPLE.pdf


https://www.quora.com/What-courses-should-I-take-for-exam-P-of-the-actuarial-exams


http://rixisu.6d4cf9adb07.bocesawo.pw/oEt2eabbJKYqDfJYMPs/


https://math.stackexchange.com/questions/1189145/recommended-textbook-to-prepare-for-exam-p


https://github.com/kmario23/deep-learning-drizzle

Good Certification that i think i will do

From KD Nuggets Website list of certifications : https://www.kdnuggets.com/education/analytics-data-mining-certificates.html


Australia Analytics Credential : https://www.iapa.org.au/resources/article/first-analytics-credential-in-australia-launched

Microsoft Certificate of Data Science: https://www.edx.org/microsoft-professional-program-data-science

University of Pennsylvania: https://www.coursera.org/specializations/business-analytics

Rice University: https://www.coursera.org/specializations/business-statistics-analysis

Essec School : https://www.coursera.org/specializations/strategic-analytics

Master of Business Administration :

  1. https://www.coursera.org/specializations/strategic-leadership
  2. https://www.coursera.org/degrees/imba

Emory University Foundations of Marketing Analytics Specialization: https://www.coursera.org/specializations/marketing-analytics

EDX Data Science Courses : https://www.edx.org/course/subject/data-science

University of Colarado : https://www.coursera.org/specializations/data-analytics-business

PwC : https://www.coursera.org/specializations/pwc-analytics?


Specialization : https://www.coursera.org/specializations/data-analytics-business

https://www.coursera.org/learn/text-mining-analytics

https://www.coursera.org/learn/predictive-analytics

https://www.coursera.org/learn/hypothesis-testing-confidence-intervals



https://www.coursera.org/courses?query=analytics&indices%5Btest_all_products%5D%5BrefinementList%5D%5Blanguage%5D%5B0%5D=English&indices%5Btest_all_products%5D%5BrefinementList%5D%5BproductDifficultyLevel%5D%5B0%5D=Advanced&indices%5Btest_all_products%5D%5Bpage%5D=1&indices%5Btest_all_products%5D%5Bconfigure%5D%5BclickAnalytics%5D=true&indices%5Btest_all_products%5D%5Bconfigure%5D%5BhitsPerPage%5D=10&configure%5BclickAnalytics%5D=true

Advance level courses: 

https://www.coursera.org/specializations/aml

https://www.coursera.org/learn/linear-models-2

https://www.coursera.org/specializations/advanced-data-science-ibm



Management Courses : 

https://www.coursera.org/specializations/marketing-mix

https://www.coursera.org/specializations/strategic-analytics

https://www.coursera.org/specializations/understanding-modern-finance

https://www.coursera.org/learn/business-strategies

https://www.coursera.org/learn/business-model

https://www.coursera.org/learn/case-studies-business-analytics-accenture

https://www.coursera.org/learn/strategic-management

https://www.coursera.org/learn/brand-management

https://www.coursera.org/learn/strategic-business-analytics



Data Science Certification as per CIO Magazine : 

Some popular data science certifications include the following:

  • Certified Analytics Professional (CAP) – The Cap Program
  • Certified Specialist in Predictive Analytics (CSPA) – The CAS Institute
  • Cloudera Certified Professional: CCP Data Engineer – Cloudera
  • Data Science Certificate – Harvard Extension School
  • DASCA Data Science Credentials – Data Science Council of America
  • IAPA Analytics Credentials – IAPA
  • SAS Academy for Data Science – SAS Institute
  • SAS Certified Big Data Professional/Data Scientist – SAS Institute
  • Simplilearn Data Science Certification Training – Simplilearn
  • Teradata Aster Analytics Certification – Teradata


Data Science Degree Programs : 

  • Master of Science in Statistics: Data Science at Stanford University
  • Master of Information and Data Science: Berkeley School of Information
  • Master of Computational Data Science: Carnegie Mellon University
  • Master of Science in Data Science: Harvard University John A. Paulson School of Engineering and Applied Sciences
  • Master of Science in Data Science: University of Washington
  • Master of Science in Data Science: John Hopkins University Whiting School of Engineering
  • MSc in Analytics: University of Chicago Graham School




op 15 data science certifications

  • Applied AI with DeepLearning, IBM Watson IoT Data Science Certificate
  • Certified Analytics Professional (CAP)
  • Cloudera Certified Associate: Data Analyst
  • Cloudera Certified Professional: CCP Data Engineer
  • Data Science Council of America (DASCA)
  • Dell Technologies Data Scientist Associate (DCA-DS)
  • Dell Technologies Data Scientist Advance Analytics Specialist (DCS-DS)
  • HDP Data Science
  • IBM Certified Data Architect
  • Microsoft MCSE: Data Management and Analytics
  • Microsoft Certified Azure Data Scientist Associate
  • Microsoft Professional Program in Data Science
  • SAS Certified Advanced Analytics Professional
  • SAS Certified Big Data Professional
  • SAS Certified Data Scientist

Applied AI with DeepLearning, IBM Watson IoT Data Science Certificate

To earn IBM’s Watson IoT Data Science Certification, you’ll need some experience coding, preferably in Python, but they will consider any programming language as a place to start. Math skills, especially with linear algebra, are recommended but the course promises to cover the topics within the first week. It’s aimed at those with more advanced data science skills and classes are offered through Coursera.

Cost: $49 per month for a subscription to Coursera
Location: Online
Duration: Self-paced
Expiration: Does not expire

Certified Analytics Professional (CAP)

CAP offers a vender-neutral certification and promises to help you “transform complex data into valuable insights and actions,” which is exactly what businesses are looking for in a data scientist: someone who not only understands the data but can draw logical conclusions and then express to key stakeholders why those data points are significant. If you’re new to data analytics, you can start with the entry-level Associate Certified Analytics Professional (aCAP) exam and then move on to your CAP certification.

Cost: $495 for INFORMS members, $695 for non-members; team pricing for organizations is available on request
Location: In person at designated test centers
Duration: Self-paced
Expiration: Valid for three years

[ Looking to upgrade your career in tech? This comprehensive online course teaches you how. ]

Cloudera Certified Associate: Data Analyst

The CCA exam demonstrates your foundational knowledge as a developer, data analyst and administrator of Cloudera’s enterprise software. Passing a CCA exam and earning your certification will show employers that you have a handle on the basic skills required to be a data scientist. It’s also a great way to prove your skills if you’re just starting out and lack a strong portfolio or past work experience.

Cost: $295 per exam specialty and per attempt
Location: Online
Duration: Self-paced
Expiration: Valid for two years

Cloudera Certified Professional: CCP Data Engineer

Once you earn your CCA, you can move on to the CCP exam, which Cloudera touts as one of the most rigorous and “demanding performance-based certifications.” According to the website, those looking to earn their CCP need to bring “in-depth experience developing data engineering solutions” to the table, as well as a “high-level of mastery” of common data science skills. The exam consists of eight to 12 customer problems that you will have to solve hands-on using a Cloudera Enterprise cluster. The exam lasts 120 minutes and you’ll need to earn a 70 percent or higher to pass.

Cost: $600 per attempt — each attempt includes three exams
Location: Online
Duration: Self-paced
Expiration: Valid for three years

Data Science Council of America (DASCA)

The Data Science Council of America offers a data scientist certification that was designed to address “credentialing requirements of senior, accomplished professionals who specialize in managing and leading Big Data strategies and programs for organizations,” according to DASCA. The certification track includes paths for earning your Senior Data Scientist (SDS) and the more advanced Principal Data Scientist (PDS) credentials. Both exams last 100 minutes and consist of 85 and 100 multiple-choice questions for the SDS and PDS exams, respectively. You’ll need at least six or more years of big data analytics or engineering experience to start on the SDS track and 10 or more years of experience to qualify for the PDS exam.

Cost: $520 per exam
Location: Online
Duration: Self-paced
Expiration: 5 years

Dell Technologies Data Scientist Associate (DCA-DS)

The DCA-DS certification is an entry-level data science designation that is designed for those new to the industry or who want to make a career switch to work as a data scientist. While the exam is designed for those without a strong background in machine learning, statistics, math or analytics, it’s still a requirement for the more advanced certification. So even if you’re already an experienced data scientist, you’ll still need to pass this exam before you can move on to the Advanced Analytics Specialist designation.

Cost: $230 per Proven Professional certification exam; you’ll also need to purchase any books or other course material
Location: Online via Pearson VUE
Duration: Self-paced
Expiration: Does not expire

Dell Technologies Data Scientist Advanced Analytics Specialist (DCS-DS)

The DCS-DS certification builds on the entry-level associate certification and covers general knowledge of big data analytics across different industries and technologies. It doesn’t specifically focus on one product or industry, so it’s a good option if you aren’t sure where you want to go with your data career or if you just want a more generalized certification for your resume. The exam covers advanced analytical methods, social network analysis, natural language processing, data visualization methods and popular data tools like Hadoop, Pig, Hive and HBase.

Cost: $230 per Proven Professional certification exam; you’ll also need to purchase any books or other course material
Location: Online via Pearson VUE
Duration: Self-paced
Expiration: Does not expire

HDP Data Science

The HDP Data Science certification course from Hortonworks covers data science topics like machine learning and natural language processing. It also covers popular concepts and algorithms used in classification, regression, clustering, dimensionality reduction and neural networks. The course will also get you up to speed on the latest tools and frameworks, including Python, NumPy, pandas, SciPy, Sckikit-learn, NLTK, TensorFlow, Jupyter, Spark MLlib, Stanford CoreNLP, TensorFlowOnSpark/Horovod/MLeap and Apache Zeppelin. The course includes a combination of lecture and discussion and the other half consists of hands-on labs, which you’ll complete before taking the exam.

Cost: $250 per attempt
Location: Online
Duration: 4 days
Expiration: 2 years

IBM Certified Data Architect

IBM’s Certified Data Architect certification isn’t for everyone — it’s geared toward seasoned professionals and experts in the field. IBM recommends that you have knowledge of the data layer and associated risk and challenges, cluster management, network requirement, important interfaces, data modeling, latency, scalability, high availability, data replication and synchronization, disaster recovery, data lineage and governance, LDAP security and general big data best practices. You will also need prior experience with software such as BigInsights, BigSQL, Hadoop and Cloudant (NoSQL), among others. You can see the long list of prerequisites on IBM’s website, but it’s safe to say you’ll need a solid background in data science to qualify for this exam.

The certification exam consists of 55 questions and five sections focusing on requirements (16%), use cases (46%), applying technologies (16%), recoverability (11%) — you will have 90 minutes to complete the exam. IBM offers web-based and in-classroom training courses on InfoSphere BigInsights, BigInsights Analytics for Programmers and Big SQL for developers.

Cost: $200 
Location: Online
Duration: 90 minutes
Expiration: N/A

Microsoft MCSE: Data Management and Analytics

MCSE certifications cover a wide variety of IT specialties and skills, including data science. For data science certifications, Microsoft offers two courses, one that focuses on business applications, and another that focuses on data management and analytics. However, each course requires prior certification under the MCSE Certification program, so you’ll want to make sure you check the requirements first.

Cost: $165 per exam, per attempt
Location: Online
Duration: Self-paced
Expiration: Valid for three years

Microsoft Certified Azure Data Scientist Associate

The Azure Data Scientist Associate certification from Microsoft focuses your ability to utilize machine learning to “train, evaluate and deploy models that solve business problems,” according to Microsoft. Candidates for the exam are tested on machine learning, AI solutions, natural language processing, computer vision and predictive analytics. The exam focuses on defining and preparing the development environment, data modeling, feature engineering and developing models.

Cost: $165 
Location: Online
Duration: Self-paced
Expiration: Credentials do not expire

Microsoft Professional Program in Data Science

The Microsoft Professional Program in Data Science focuses on eight specific data science skills, including T-SQL, Microsoft Excel, PowerBI, Python, R, Azure Machine Learning, HDInsight and Spark. Microsoft claims there are over 1.5 million open jobs looking for these skills. Courses run for three months every quarter and you don’t have to take them in order; it’s self-paced with a recommended commitment of two to four hours per week.

Cost: Must purchase credits through EdX, some materials are free
Location: Online
Duration: 6 weeks
Expiration: Does not expire

SAS Certified Advanced Analytics Professional

This program covers machine learning, predictive modeling techniques, working with big data sets, finding patterns, optimizing data techniques and time series forecasting. The certification program consists of nine courses and three exams that you’ll have to pass to earn the designation. You’ll need at least six months of programming experience in SAS or another language and it’s also recommended that you have at least six months of experience using mathematics or statistics in a business setting.

Cost: $299 per month subscription 
Location: Online
Duration: Self-paced
Expiration: Credentials do not expire

SAS Certified Big Data Professional

The SAS Big Data certification includes two modules with a total of nine courses. You’ll need to pass two exams to earn the designation. The course covers SAS programming skills, working with data, improving data quality, communication skills, fundamentals of statistics and analytics, data visualization and popular data tools such as Hadoop, Hive, Pig and SAS. To qualify for the exam, you’ll need at least six months of programming experience in SAS or another language.

Cost: $299 per month subscription 
Location: Online
Duration: Self-paced
Expiration: Credentials do not expire

SAS Certified Data Scientist

The SAS Certified Data Scientist certification is a combination of the other two data certifications offered through SAS. It covers programming skills, managing and improving data, transforming, accessing and manipulating data and how to work with popular data visualization tools. Once you earn both the Big Data Professional and Advance Analytics Professional certifications, you can qualify to earn your SAS Certified Data Scientist designation. You’ll need to complete all 18 courses and pass the five exams between the two separate certifications.

Cost: $299 per month subscription 
Location: Online
Duration: Self-paced

Expiration: Credentials do not expire

R links

Data Analysis for Life Sciences : https://www.edx.org/xseries/data-analysis-life-sciences

Podcast by Data Scientist : https://soundcloud.com/nssd-podcast

Data Visualization : http://socviz.co/

R CookBook : http://www.cookbook-r.com/

GGPLOT2 Mastery : https://github.com/hadley/ggplot2-book

R packages : http://r-pkgs.had.co.nz/

Advanced R : http://adv-r.had.co.nz/

R for Data Sciences : https://r4ds.had.co.nz/

Text Mining : https://www.tidytextmining.com/

Fundamentals of Data Visualization : https://serialmentor.com/dataviz/

STAT 545 Notes University of British Colombia : https://github.com/STAT545-UBC

The Caret Package : http://topepo.github.io/caret/