My talk_ Using data to get business insights
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The document discusses the integration of data and business insights in various sectors like banking, retail, healthcare, and human resources, emphasizing the importance of data quality and analytics. It highlights key analytics types, challenges related to employee churn, and explores the gender pay gap, along with necessary technical skills for data professionals. Dr. Nisha Arora's contributions to data science education and upcoming talks are also mentioned.
My talk_ Using data to get business insights
- 1. ALPINE SKI HOUSE USING DATA TO GET BUSINESS INSIGHTS What matters most?
- 2. ALPINE SKI HOUSE ALPINE SKI HOUSE 2
- 3. ALPINE SKI HOUSE About Me Data, Business How do these connect? Q/A Session AGENDA
- 4. ALPINE SKI HOUSE • Dr. Nisha Arora is a proficient educator, passionate trainer,You Tuber, occasional writer, and a learner forever. ✓ PhD in Mathematics. ✓ Works in the area of Data Science, Statistical Research, Data Visualization & Storytelling ✓ Creator of various courses ✓ Contributor to various research communities and Q/A forums ✓ Mentor for women in Tech Global 4 ABOUT ME An educator by heart & a trainer by profession.
- 5. ALPINE SKI HOUSE http://stats.stackexchange.com/users/79100/learner https://stackoverflow.com/users/5114585/dr-nisha-arora https://www.quora.com/profile/Nisha-Arora-9 https://www.researchgate.net/profile/Nisha_Arora2/contributions http://learnerworld.tumblr.com/ https://www.slideshare.net/NishaArora1 https://scholar.google.com/citations?user=JgCRWh4AAAAJ&hl=en&authuser=1 https://www.youtube.com/channel/UCniyhvrD_8AM2jXki3eEErw https://groups.google.com/g/dataanalysistraining/search?q=nisha%20arora https://www.linkedin.com/in/drnishaarora/detail/recent-activity/posts/ ✓ Research Queries ✓ Coding Queries ✓ Blog Posts ✓ Slide Decks ✓ My Talks ✓ Publications ✓ Lectures ✓ Layman’s Term Explanation ✓ Mentoring ✓ Articles & Much More MY CONTRIBUTION TO THE COMMUNITY
- 6. ALPINE SKI HOUSE Connect With Me https://www.linkedin.com/in/drnishaarora/ Dr.aroranisha@gmail.com .
- 7. ALPINE SKI HOUSE My Previous Talk ✓ Is Data Science right career choice for you? ✓ Data Science_ Hype Vs Reality ✓ Data Science Job Roles ✓ Required Technical Skills ✓ Getting a Dream Job
- 8. ALPINE SKI HOUSE My Upcoming Talk Becoming a Data Professional https://www.youtube.com/playlist?list=PLf ZXabiF_17rt0NuQ-l8YTMMwGdE9zMcj
- 9. ALPINE SKI HOUSE ALPINE SKI HOUSE BUSINESS INSIGHTS ✓ What is that? ✓ How does data help? ✓ How important is data quality? ✓ What are the skills needed?
- 10. ALPINE SKI HOUSE ALPINE SKI HOUSE USE CASES 10
- 11. ALPINE SKI HOUSE ALPINE SKI HOUSE BANKING ✓ Customer Segmentation & Profiling ✓ Product Pricing ✓ Recommending Right Product ✓ Default Prediction ✓ Reducing Manual Review ✓ Reduce Attrition ✓ Predict Interest Rate 11
- 12. ALPINE SKI HOUSE ALPINE SKI HOUSE RETAIL ✓ Customer Segmentation & Profiling ✓ Recommendation Engines ✓ Personalized Marketing ✓ Pricing ✓ Anticipatory Shipping 12
- 13. ALPINE SKI HOUSE ALPINE SKI HOUSE HEALTH CARE ✓ Imaging ✓ Drug discover/ Clinical Trails ✓ Tracking & Preventing Disease ✓ Providing Virtual Assistance 13
- 14. ALPINE SKI HOUSE ALPINE SKI HOUSE LOGISTICS ✓ Route Optimization ✓ Smart Warehouse ✓ Market Forecasting ✓ Reducing Freight Costs 14
- 15. ALPINE SKI HOUSE ALPINE SKI HOUSE SPORTS ✓ Ticket Pricing ✓ Player Selection ✓ Fan Sentiment Analysis https://www.informs.org/ORMS-Today/Public-Articles/February-Volume-44-Number- 1/The-Sports-Analytics-Explosion 15
- 16. ALPINE SKI HOUSE ALPINE SKI HOUSE HUMAN RESOURCE ✓ Optimal Staffing ✓ Objective Performance management ✓ Accurate record of company’s progress ✓ Analyze Gender wage gap ✓ Job Satisfaction ✓ Understanding productivity & motivations ✓ Policy Effectiveness ✓ Absenteeism ✓ Employee Churn Prediction 16
- 17. ALPINE SKI HOUSE ALPINE SKI HOUSE TYPE OF ANALYTICS ✓ Descriptive – Explain what happed ✓ Diagnostic – Explain why it happen ✓ Predictive – Forecast what will happen in future ✓ Prescriptive – Recommends action based on forecast 17
- 18. ALPINE SKI HOUSE ALPINE SKI HOUSE DATA DRIVEN DECISIONS MAKING 18
- 19. ALPINE SKI HOUSE ALPINE SKI HOUSE DATA COLLECTION 19 Demographics Pay Engagement Performance ATS Survey Data
- 20. ALPINE SKI HOUSE ALPINE SKI HOUSE DATA COLLECTION 20 On-boarding Employee Profile Training Retention Turnover/Churn Absenteeism
- 21. ALPINE SKI HOUSE ALPINE SKI HOUSE HR ANALYTICS METRICS 21 Time to hire Recruitment cost to hire Turnover/Churn Absenteeism Engagement Rating
- 22. ALPINE SKI HOUSE ALPINE SKI HOUSE WHAT DO YOU SEE? 22
- 23. ALPINE SKI HOUSE EMPLOYEE CHURN Who are likely to leave? What are the key reasons/events? Is there a specific work accident/event that cause turnover? What actions can company take to prevent employee turnover? How can company retain it’s valuable employees? Can providing training/support/change role/department help? 23
- 24. ALPINE SKI HOUSE EMPLOYEE CHURN 24 Business Problem(s) ✓ Ongoing project suffering ✓ High cost to hire & train new employee ✓ Difficulty in finding right talent ✓ New employees cost more ✓ New employees take time to join/ get into project ✓ Damage to reputation Data Problem(s) ✓ Identify factors affecting churn ✓ Predict employee churn ✓ Looking for something unsual/unexpected event/cause
- 25. ALPINE SKI HOUSE EMPLOYEE CHURN 25 Stockholders ✓ Hiring managers ✓ Resource Managing Team ✓ Training & Development Department ✓ Project Manager ✓ Technical Manager Success Matrix ✓ Employee churn rate (to reduce) ✓ Cost of turnover (to reduce) ✓ First-year- resignation rate ✓ Engagement rate ✓ Satisfaction Rate ✓ Employee training cost/per employee
- 26. ALPINE SKI HOUSE DEMYSTIFYING GENDER PAY GAP What is gender pay gap? How to compute gender pay gap? What causes gender pay gap? Do we really need to worry about it? 26
- 27. ALPINE SKI HOUSE GENDER WAGE GAP 27 Purpose ✓ Identify gender pay gap ✓ Factors causing gender pay gap ✓ Consequences of gender pay gap ✓ What actions can be taken to rationalize gender pay gap? Benefits ✓ The ability to attract and retain the best people ✓ The promotion of gender equality ✓ The opportunity to enhance public perceptions of the kind of organization you are, and to avoid reputational damage
- 28. ALPINE SKI HOUSE ALPINE SKI HOUSE FURTHER READINGS Glassdoor survey Harvard Study Australia’s Gender Gap Statistics 28
- 29. ALPINE SKI HOUSE ALPINE SKI HOUSE WHAT DO YOU SEE? 29 Name Gender Department Salary Loc Rating 0 Ches Bonnell Male Sales $88,050 Bellevue Very Good 1 Garwin Peasegood Female Engineering $68,220 Bellevue Good 2 Sidoney Yitzhok Female NaN $118,440 Wellington Not Rated 3 Saunders Blumson NaN Legal $56,370 Los Angeles Very Good 4 Gardy Grigorey Female Support $107,090 Los Angeles Poor
- 30. ALPINE SKI HOUSE ALPINE SKI HOUSE WHAT DO YOU SEE? 30
- 31. ALPINE SKI HOUSE ALPINE SKI HOUSE WHAT DO YOU SEE? 31
- 32. ALPINE SKI HOUSE ALPINE SKI HOUSE WHAT DO YOU SEE? 32
- 33. ALPINE SKI HOUSE ALPINE SKI HOUSE WHAT DO YOU SEE? 33
- 34. ALPINE SKI HOUSE ALPINE SKI HOUSE WHAT DO YOU SEE? 34
- 35. ALPINE SKI HOUSE ALPINE SKI HOUSE WHAT DO YOU SEE? 35 For the year 2013, the gender pay gap in India was estimated to be 24.81%.
- 36. ALPINE SKI HOUSE ALPINE SKI HOUSE REQUIRED TECHNICAL SKILLS Data Analysts Data Scientists Data Engineer Excel Statistical Analysis Python/ R Databases Data Viz & Storytelling SQL/ Databases R/Python/SAS EDA/Data Wrangling/Data Prep Data Warehousing/Data Architecture Tableau/PowerBI Machine Learning Distributed Computing (Hadoop/Map Reduce) Statistical Analysis SQL Data Management & Data Security Data Viz & Storytelling R, Python, SAS (sometimes) Docker, Kubernaties,Kafka, Spark,Git GitHub EDA/Data Wrangling/Data Prep Tensorflow, Keras,Torch Cloud Tech (AWS/Azure/GCP) Dashboards & Reporting Git/ GitHub 36 From my talk
- 37. ALPINE SKI HOUSE ALPINE SKI HOUSE REQUIRED SKILLS Tools Techniques Skills Excel EDA/Data Wrangling/Data Prep Domain Knowledge Databases Data Viz Communication Python/R/SAS Dashboarding & Reports Curiosity Tableau/PowerBI Statistical Analysis Adaptability/Learnability BI/ETL Tools ML/DS Business Acumen Git/Git Hub Data Warehousing/Data Architecture Team Player Docker, Kubernaties,Kafka, Spark Distributed Computing (Hadoop/Map Reduce) Storytelling Cloud Tech (AWS/Azure/GCP) Data Management & Data Security Love for Data Crunching 37 Becoming a data professional
- 38. ALPINE SKI HOUSE ALPINE SKI HOUSE END-TO-END ML PROJECT LIFECYCLE 1. Look at the big picture 2. Get the data 3. Discover and visualize the data to gain insights 4. Prepare the data for Machine Learning algorithms 5. Select a model and train it 6. Fine-tune your model 7. Present your solution 8. Launch, monitor, and maintain your system 38
- 39. ALPINE SKI HOUSE The only impossible journey is the one you never begin
- 40. ALPINE SKI HOUSE Python Pandas for Business Analytics/ Data Science _ Level 1
- 41. ALPINE SKI HOUSE Python IDEs Python Basics for Absolute Beginners 30 Videos & Counting Code Files Exercises One Online Course & 2 more in progress Live Classes offered
- 42. ALPINE SKI HOUSE THANK YOU VICTORIA LINDQVIST +1 (589) 555‐0199 victoria@alpineskihouse.c om