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Top 10 things Executives should know to get a Return on your Data Science Investment

Director at Toptier Consulting Pty Ltd - Accelerated Foresight

Top 10 things Executives should know to get a Return on your Data Science Investment

- 1. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATIONNEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Top 10 “Data Science Takeaways” for Executives August 2015
- 2. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Is this just Hype?
- 3. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Top 10 “Data Science Takeaways” for Executives 1. What is Data Science and what is a Data Scientist? 2. Why is it important for executives to know about Data Science? 3. How to work through a problem like a Data Scientist. 4. How to Spot a Data Scientist: Top 10 traits and skills of a Data Scientist. 5. 5 tips for building a great Data Science team. 6. 5 things to know about ‘Machine Learning’. 7. 10 Aspects of Data Science to Cover in your Project. 8. Top 5 Sources of External Data. 9. 5 Questions to Ask if you need a Data Scientist? 10. We can help.
- 4. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Top 10 “Data Science Takeaways” for Executives 1. What is Data Science and what is a Data Scientist? 2. Why is it important for executives to know about Data Science? 3. How to work through a problem like a Data Scientist. 4. How to Spot a Data Scientist: Top 10 traits and skills of a Data Scientist. 5. 5 tips for building a great Data Science team. 6. 5 things to know about ‘Machine Learning’. 7. 10 Aspects of Data Science to Cover in your Project. 8. Top 5 Sources of External Data. 9. 5 Questions to Ask if you need a Data Scientist? 10. We can help.
- 5. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION
- 6. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION
- 7. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Who Coined the Term ‘Data Science’? 2001 William S Cleveland Professor of Stats and Computer Science Purdue University Published: “Data Science” An Action Plan for Expanding the Technical Areas of the Field of Statistics Because the plan is ambitious and implies substantial change, the altered field will be called ‘Data Science’ the knowledge among computer scientists about how to think of and approach the analysis of data is limited just as the knowledge of computing environments by statisticians is limited. A mergerof knowledge bases would produce a powerful force for innovation.
- 8. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 2009 Hal Varian Chief Economist: Google “I keep saying the sexy job in the next ten years will be statisticians.” The ability to take data To be able to understand it To process it to extract value from it To visualise it To communicate it that’s going to be a hugely important skill in the next decades
- 9. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 2010 Kenneth Cukier The Economist Special Report “Data, Data Everywhere” “… a new kind of professional has emerged, the Data Scientist Who combines the skills of statistician And storyteller artist Software programmer, … to extract the nuggets of goldhidden under mountains of data.”
- 10. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 2010 Mike Loukides Author, “What is Data Science” O’Reilly Media “Data scientists combine entrepreneurship with patience the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: ‘here’s a lot of data, what can you make from it’ ?
- 11. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 2010 Hilary Mason – Founder, Fast Forward Labs Chris Wiggins – Chief Data Scientist at New York Times Authors of “A Taxonomy of Data Science” The OSEMiN process Data science is clearly a blend of the hackers’ arts… statistics and machine learning… and the expertise in mathematics and the domain of the data for the analysis to be interpretable… It requires creative decisions and open-mindedness in a scientific context.”
- 12. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 2011 “Ever tried. Ever failed. No matter. Try Again. Fail again. Fail better.” – Irish Poet Samuel Beckett Pete Warden Google Engineer Google acquired Jetpac in 2014 “There is no widely accepted boundary for what’s inside and outside of data science’s scope. Is it just a faddish rebranding of statistics? I don’t think so, but I also don’t have a full definition. I believe that the recent abundance of data has sparked something new in the world, and when I look around I see people with shared characteristics who don’t fit into traditional categories. These people tend to work beyond the narrow specialties that dominate the corporate and institutional world, handling everything from finding the data, processing it at scale, visualizing it and writing it up as a story … (next page)
- 13. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 2011 “Ever tried. Ever failed. No matter. Try Again. Fail again. Fail better.” – Irish Poet Samuel Beckett Pete Warden Google Engineer Google acquired Jetpac in 2014 They also seem to start by looking at what the data can tell them, , and then picking interesting threads to follow, rather than the traditional scientist’s approach of choosing the problem first and then finding data to shed light on it.”
- 14. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 2011DJ Patil – Chief Scientist LinkedIn Jeff Hammerbacher Bear Sterns Quant Facebook Authors of “Building Data Science Teams” We realized that as our organizations grew, we both had to figure out what to call the people on our teams? • ‘Business analyst’ seemed too limiting. • ‘Data analyst’ was a contender, but we felt that title might limit what people could do. After all, many of the people on our teams had deep engineering expertise. • ‘Research scientist’ was a reasonable job title used by companies like Sun, HP, Xerox, Yahoo, and IBM. • However, we felt that most research scientists worked on projects that were futuristic and abstract […] Instead, the focus of our teams was to work on data applications that would have an immediate and massive impact on the business. The term that seemed to fit best was data scientist: those who use both data and science to create something new. “
- 15. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION In Summary, what is Data Science and what is a Data Scientist? Computer Science Mathematics Statistics StorytellerArtist Outside the Box Interdisciplinary ‘here’s a lot of data, what can you make from it’ immediate and massive impact on the business. Open Mindedness Nuggetsofgold
- 16. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Are we looking for a Unicorn? Let’s delve deeper
- 17. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Top 10 “Data Science Takeaways” for Executives 1. What is Data Science and what is a Data Scientist? 2. Why is it important for executives to know about Data Science? 3. How to work through a problem like a Data Scientist. 4. How to Spot a Data Scientist: Top 10 traits and skills of a Data Scientist. 5. 5 tips for building a great Data Science team. 6. 5 things to know about ‘Machine Learning’ 7. 10 Aspects of Data Science to Cover in your Project 8. Top 5 Sources of External Data 9. 5 Questions to Ask if you need a Data Scientist? 10. We can help
- 18. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Leading Companies that Employ Data Scientists in Senior Positions http://www.forbes.com/sites/caroltice/2014/12/29/1-billion-in-under-5-years-the-12-hottest-tech-startups/
- 19. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION America’s Largest Supermarket 2625 Stores $100b Revenue Examples of Data Science Successes 2000+ Employee Data Science Consultancy UK Based The company accesses, collects, and manages data for about 770 million consumers. For Kroger, the analytics output from big data has helped them see greater, more actionable insights on customer loyalty and profitability. 95% of sales are rung up on the loyalty card, Kroger sees an impact from its award-winning loyalty program through nearly 60% redemption rates and over $12 billion in incremental revenue by using big data and analytics since 2005.
- 20. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Examples of Data Science Successes On a daily basis, UPS makes 16.9m package and document deliveries every day and over 4 billion items shipped per year through almost 100,000 vehicles. With this volume, there are numerous ways UPS uses big data, and one of the applications is for fleet optimization. On-truck telematics and advanced algorithms help with routes, engine idle time, and predictive maintenance. Since starting the program, the company has saved over 39 million gallons of fuel and avoided driving 364 million miles. The next steps include completion of the roll- out and applying the operational efficiency to their airplanes.
- 21. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Examples of Data Science Successes The American Express Company looked to shift traditional business intelligence-based hindsight reporting or trailing indicators of how business was doing to predict loyalty. Their sophisticated predictive models analyzed historical transactions with 115 variables to forecast potential churn. In the Australian market, they now believe they can identify 24% of accounts that will close within four months.
- 22. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Data Science Applications in Banks Financial Compliance Liquidity Management Dialogue with Consumers Customer Churn New Revenue Streams Fraud Competitor Analysis Call Centre Optimisation
- 23. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Top 10 “Data Science Takeaways” for Executives 1. What is Data Science and what is a Data Scientist? 2. Why is it important for executives to know about Data Science? 3. How to work through a problem like a Data Scientist. 4. How to Spot a Data Scientist: Top 10 traits and skills of a Data Scientist. 5. 5 tips for building a great Data Science team. 6. 5 things to know about ‘Machine Learning’ 7. 10 Aspects of Data Science to Cover in your Project 8. Top 5 Sources of External Data 9. 5 Questions to Ask if you need a Data Scientist? 10. We can help
- 24. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION How to Work Through a Problem Like a Data Scientist Obtain Data Scrub Data Explore Data Model Data iNterpret Results The OSEMiN Process
- 25. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION How to Work Through a Problem Like a Data Scientist Key points to keep in mind: • Manual processes do not scale (i.e. Excel) • Internal Data: – Develop scripts in Python, SQL, Shell scripts etc • External Data: – USE API’s, e.g JSON or XML syntax – Use permalinks – Web Scraping tools can also work. • Must be able to repeat at the press of a button Obtain Data
- 26. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION How to Work Through a Problem Like a Data Scientist Key points to keep in mind: • Data will always be messy • It’s the least sexy but can result in the greatest benefit • Can be up to 80% of time spent – E.g., “New York, NY”, “NYC”, “New York City”, “Manhattan, NY”, “The Big Apple”. • Again, Perl or Python is the #1 choice with Data Scientists – Other text manipulation tools: Sed, awk, grep Scrub Data “A simple analysis of clean data can be more productive than a complex analysis of noisy and irregular data.”
- 27. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION How to Work Through a Problem Like a Data Scientist Key points to keep in mind: • This is where there is no hypothesis that is being tested and no predictions that are being evaluated. • Mainly involves: – Visualizing (histograms and scatterplots) – Clustering (k-means) – Performing dimensionality reduction (banking example) • Take notes. Take lots of notes! • The point is to understand, not to produce a report. Explore Data
- 28. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION How to Work Through a Problem Like a Data Scientist Key points to keep in mind: • “often the ‘best’ model is the most predictive model” • So, hide some of the data! (Chinese Wall) • “all models are wrong, but some are useful”. Pick the ‘least bad’ one. Model Data
- 29. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION How to Work Through a Problem Like a Data Scientist Key points to keep in mind: • “the purpose of computing is insight, not numbers” • The best predictive models aren’t always the best interpretive models. The best interpretive models are often the simplest. • Domain (industry) expertise and intuition is often better here than technical expertise. iNterpret Results
- 30. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Interpretation vs Prediction iNterpret Results Be careful of isolated correlations
- 31. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION How to Work Through a Problem Like a Data Scientist Obtain Data Scrub Data Explore Data Model Data iNterpret Results The OSEMiN Process ‘Hacking’ Skills Maths and Stats Skills Domain Knowledge This is where the 3 skill groups of a data scientist overlap with the process
- 32. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Top 10 “Data Science Takeaways” for Executives 1. What is Data Science and what is a Data Scientist? 2. Why is it important for executives to know about Data Science? 3. How to work through a problem like a Data Scientist. 4. How to Spot a Data Scientist: Top 10 traits and skills of a Data Scientist. 5. 5 tips for building a great Data Science team. 6. 5 things to know about ‘Machine Learning’ 7. 10 Aspects of Data Science to Cover in your Project 8. Top 5 Sources of External Data 9. 5 Questions to Ask if you need a Data Scientist? 10. We can help
- 33. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION How to Spot a Data Scientist Top 10 traits and skills of a Data Scientist. Obtain Data Scrub Data Explore Data Model Data iNterpret Results 1 Be Curious (why, why, why) 2 70% of Data Scientists have Engineering, Maths, Science or Computer Science Degrees 3 Python andor R andor SAS 4 SQL 7 Hadoop andor Hive andor Pig 8 Unstructured Data (Graph, NoSQL, Twitter, FB) 9 Cloud (Amazon or Azure) 5&6 Communication Skills Business Acumen 10 Team Player
- 34. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Top 10 “Data Science Takeaways” for Executives 1. What is Data Science and what is a Data Scientist? 2. Why is it important for executives to know about Data Science? 3. How to work through a problem like a Data Scientist. 4. How to Spot a Data Scientist: Top 10 traits and skills of a Data Scientist. 5. 5 tips for building a great Data Science team. 6. 5 things to know about ‘Machine Learning’ 7. 10 Aspects of Data Science to Cover in your Project 8. Top 5 Sources of External Data 9. 5 Questions to Ask if you need a Data Scientist? 10. We can help
- 35. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 5 tips for building a great Data Science Team #1: Stop Hunting Unicorns: • There is a path to data science success that does not involve seeking that one perfect person • Assemble a team of people with strong foundational skills and a drive for success • Begin building your team with data engineers, project managers, domain experts, machine learning experts, and data modelers. • Fill some roles from within your ranks; by incorporating existing talent, you’ll have some team members with business and domain knowledge • If you find that data scientist, great!
- 36. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 5 tips for building a great Data Science Team #2: Provide Proper Infrastructure: • Data science requires more than brilliant minds; you need a solid data infrastructure. • Nothing frustrates a data science effort more than having to wait for tools to become available. • Spend some time getting infrastructure in place so that your team can start having an impact right away.
- 37. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 5 tips for building a great Data Science Team #3: Have a Compass: • Hire smart people turn them loose on data wait for great things to happen? No. • Align efforts with business goals • Form a hypothesis, e.g. “we believe that by looking across multiple channels we can better understand our customers, improve their satisfaction and increase their lifetime value.” • If you have established clear objectives and deliverables, it is very clear whether your data science team is delivering results.
- 38. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 5 tips for building a great Data Science Team #4: Have a Timetable: • It is vital in a corporate data science program to translate ideas into real world results. • Unlike pure research where publication is the benchmark for success, business demands iteration and delivery. • It’s important to build data science teams with people who are not only very smart, but who excel at getting things done. • Assign a project manager to your data science team to track project efforts and deliver at a steady pace.
- 39. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 5 tips for building a great Data Science Team #5: Learn to spot success: • One of the first signs that a data science operation is on the right path is when there is clear collaboration among the business analysts, data engineers, and data scientists. • Look for them to be working cross-functionally on projects. • If they are busy and their capacity is starting to be strained, that is an indication that good ideas are being tested from multiple angles.
- 40. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Top 10 “Data Science Takeaways” for Executives 1. What is Data Science and what is a Data Scientist? 2. Why is it important for executives to know about Data Science? 3. How to work through a problem like a Data Scientist. 4. How to Spot a Data Scientist: Top 10 traits and skills of a Data Scientist. 5. 5 tips for building a great Data Science team. 6. 5 things to know about ‘Machine Learning’ 7. 10 Aspects of Data Science to Cover in your Project 8. Top 5 Sources of External Data 9. 5 Questions to Ask if you need a Data Scientist? 10. We can help
- 41. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 5 things to know about ‘Machine Learning’ #1: Why must I know about Machine Learning? • It is a BIG part of Data Science. • Many of the data modeling methods used are classified under ‘machine learning’. • It sounds complicated and futuristic, but it’s just a new word for things computers have been doing for a while. • The ‘futuristic’ bit is that computing power is making these algorithms a LOT more useful in recent years.
- 42. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 5 things to know about ‘Machine Learning’ #2: Definition of Machine Learning Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.
- 43. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Obtain Data Scrub Data Explore Data Model Data iNterpret Results 5 things to know about ‘Machine Learning’ #3: Where does it fit into the OSEMiN process?
- 44. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 5 things to know about ‘Machine Learning’ #4: There are 3 main styles of Machine Learning Supervised Learning Data with a known result. BANKING EXAMPLE Curve fitting a history of transaction volumes using linear regression. Unsupervised Learning Data with no known result. BANKING EXAMPLE Finding a new way to group customers using k- means analysis. Semi-Supervised Learning Data with a combination of known and unknown results. EXAMPLE Image recognition. Massive datasets, very few tags
- 45. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 1. Regression Algorithms • E.g. Linear Regression • Business Application: – How many cash withdrawals will occur next month? 2. Instance Based Algorithms • E.g. K-nearest neighbours • Business Application: – If I default on my loan, what are the chances other people like me will also default? 5 things to know about ‘Machine Learning’ #5: There are 10 main types of machine learning algorithms
- 46. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 3. Regularization Algorithms • Used to reduce the number of variables in another algorithm • E.g. LASSO Method – Business Application in Hedge Funds and Genetics 4. Decision Tree Algorithms • e.g. CART (Classification and Regression Tree) • % Chance of surviving the Titanic 5 things to know about ‘Machine Learning’ #5: There are 10 main types of machine learning algorithms
- 47. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 5. Bayesian Algorithms • e.g. Naïve Bayes • It’s all about probabilities. • Example: – % Cancer Sufferers: 1% – % of People who are 65: 0.2% – % of Cancer Sufferers who are 65: 0.5% – Probability of someone who is 65 having cancer: • =(0.5% / 0.2%) * 1% • = 2.5% 6. Clustering Algorithms • e.g. K-Means • Grouping of entities based on similar characteristics • Business Application: – Customer Segmentation based on Age, Income, geography, transaction behaviour and product mix 5 things to know about ‘Machine Learning’ #5: There are 10 main types of machine learning algorithms
- 48. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 7. Association Rule Learning Algorithms • e.g. Apriori • Works well on transactional databases. • Business Application: – “If a customer buys product X and also product Y, how likely are they to buy Z?” 8. Artificial Neural Network Algorithms • Simulate the way biology makes decisions. • Used mostly for regression and classification • Business Application: – Facial and handwriting recognition. – Driverless cars – Automated Trading 5 things to know about ‘Machine Learning’ #5: There are 10 main types of machine learning algorithms
- 49. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Ensemble Algorithms: • Ways of combining of weaker algorithms to make a strong algorithm Support Vector Machines: • This is not a group, but a method which doesn’t fit into other groups. • Groups items into 1 of 2 categories by defining the line between 2 groups that is furthest from all points. • Business Application: – Predicting Stock Prices – Credit Scoring 5 things to know about ‘Machine Learning’ #5: There are 10 main types of machine learning algorithms
- 50. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Top 10 “Data Science Takeaways” for Executives 1. What is Data Science and what is a Data Scientist? 2. Why is it important for executives to know about Data Science? 3. How to work through a problem like a Data Scientist. 4. How to Spot a Data Scientist: Top 10 traits and skills of a Data Scientist. 5. 5 tips for building a great Data Science team. 6. 5 things to know about ‘Machine Learning’ 7. 10 Aspects of Data Science to Cover in your Project 8. Top 5 Sources of External Data 9. 5 Questions to Ask if you need a Data Scientist? 10. We can help
- 51. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION This diagram serves as a ‘checklist’ in each area of your data science project to ensure that you’ve considered everything, including the skills matrix of your team.
- 52. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Top 10 “Data Science Takeaways” for Executives 1. What is Data Science and what is a Data Scientist? 2. Why is it important for executives to know about Data Science? 3. How to work through a problem like a Data Scientist. 4. How to Spot a Data Scientist: Top 10 traits and skills of a Data Scientist. 5. 5 tips for building a great Data Science team. 6. 5 things to know about ‘Machine Learning’ 7. 10 Aspects of Data Science to Cover in your Project 8. Top 5 Sources of External Data 9. 5 Questions to Ask if you need a Data Scientist? 10. We can help
- 53. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Top 5 Sources of External Data 5. Amazon: aws.amazon.com/datasets/ – Astronomy, Biology, Chemistry, Climate, Economics, Encyclopedic, Geographic, Mathematics 4. US Govt: usgovxml.com/ – US Government sourced data 3. RE3Data: www.re3data.org/ – Acoustics to Zoology (European based) 2. QUANDL: www.quandl.com – Financial Instrument Prices – Economic indicators 1. Enigma: enigma.io/ – “the world’s largest and most robust store of government information.”
- 54. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Top 10 “Data Science Takeaways” for Executives 1. What is Data Science and what is a Data Scientist? 2. Why is it important for executives to know about Data Science? 3. How to work through a problem like a Data Scientist. 4. How to Spot a Data Scientist: Top 10 traits and skills of a Data Scientist. 5. 5 tips for building a great Data Science team. 6. 5 things to know about ‘Machine Learning’ 7. 10 Aspects of Data Science to Cover in your Project 8. Top 5 Sources of External Data 9. 5 Questions to Ask if you need a Data Scientist? 10. We can help
- 55. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION 5 Questions to Ask if you need a Data Scientist? 1. Do you know what Data Scientists do? 2. Do you have enough data available? 3. Do you have a specific problem to solve? 4. Can you get away with heuristic (good enough) solutions, intuition or manual processes? 5. Are you committed to being data driven? - This presentation has hopefully answered point 1 - We would be happy to assist you in points 2 to 5.
- 56. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Top 10 “Data Science Takeaways” for Executives 1. What is Data Science and what is a Data Scientist? 2. Why is it important for executives to know about Data Science? 3. How to work through a problem like a Data Scientist. 4. How to Spot a Data Scientist: Top 10 traits and skills of a Data Scientist. 5. 5 tips for building a great Data Science team. 6. 5 things to know about ‘Machine Learning’ 7. 10 Aspects of Data Science to Cover in your Project 8. Top 5 Sources of External Data 9. 5 Questions to Ask if you need a Data Scientist? 10. We can help
- 57. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Who is Toptier? We are a company which specializes in Data Science Program Strategy and Delivery. We believe you should get a ‘Return on Data Science’ investment within the 1st year of your program. We can advise, assist and provide training, resources and skills for your program, ranging from periodic strategic advice to the complete management of your program, it’s up to you. We understand what people, technology and business processes are required to make your program a success.
- 58. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Our Products and ServicesProgramStrategy • How to put together a DS program • Where to start • What technology to use • What type of people to hire • How to measure your Return on investment ProgramOutsourcing • Full management and outsourcing of your data science program • People, technology and business processes • Quarterly milestones with clear deliverables TrainingCourses • Data Science 101 For Managers • Data Science 101 For SQLBI experts • R and SQL: The Data Scientist’s tools of choice Technology • Implementati on of the tools and technology required to run a Data Science Program • Integration to existing systems • Cloud-based service or on- site implementati on options. Recruitment • Sourcing of the required staff for your Data Science program. • Coaching and Mentorship option to turn your staff into true data scientists. ConsultingServices • Data Scientists • SQL Specialists • BI specialists • R and Python Specialists • From Senior level to Junior • Available at fixed daily rates
- 59. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION Why Toptier? 90% of the time spent on a Data Science program is data manipulation and cleaning. True Data Scientists are scarce, but SQL and BI resources are not. SQL and BI resources can be trained to use data science tools. A part time Senior Data Scientist managing a few full time BISQL resources is a very cost-effective way of running a Data Science Program. Toptier offers the benefit of this expertise, without having to employ a full time senior Data Scientist resource.
- 60. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION How to engage with us Let’s Talk •Let’s discuss your requirements Let us Help •Based on your timeframes, budget, scope of work and existing skills within your organisation, we can advise you what services we can offer to suit your Data Science Program. Let’s get you going •Our initial advice is free. We will even assist you to put together an internal business proposition to your superiors on the benefits of a Data Science Program. Let’s Show Value •Many DS programs fail due to ‘data overload’ and ‘theoretical overkill’ •Let’s get your program showing a return on investment as quickly as possible •Speak to us today to find out how
- 61. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION This Presentation • http://www.slideshare.net/DylanErens/top-10-data- science-takeaways-for-executives-52125746
- 62. NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION To learn more about Toptier, please go to: www.toptier.co.za or Call us directly for an appointment at: Dylan Erens 072 179 8815 NEXT GENERATION ANALYTICS, DATA MINING, FORECASTING AND SIMULATION

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Hemos actualizado su política de privacidad para cumplir con las cambiantes normativas de privacidad internacionales y para ofrecerle información sobre las limitadas formas en las que utilizamos sus datos.

Puede leer los detalles a continuación. Al aceptar, usted acepta la política de privacidad actualizada.

¡Gracias!

Hemos encontrado un problema, inténtelo de nuevo.