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Data Science and Analytics

This is a training slide on Data Science and Analytics presented by Tobe M. (The founder of Datatechcon and an expert Data scientist).

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Data Science and Analytics

  1. 1. INTRODUCTION TO DATA SCIENCE & ANALYTICS Learn how to leverage data & analytics to help increase business value. www.datatechcon.com 1
  2. 2. 5 Progress 6 Future Plans 3 Timeline 4 Our Team INTRODUCTION 1 About Us 2 Our Services • Fortune 200 Data Scientist • Founder of Data Techcon • 10+ years experience in tech • MIT certified analytics expert • Data Subject Matter Expert at compTIA • Trained more than 500 students. www.datatechcon.com 2
  3. 3. DATA SCIENCE & ANALYTICS TERMS www.datatechcon.com 3 01. Data Science is an inter- disciplinary field that uses scientific methods & algorithms to extract insights from data. It is an umbrella term for a group of fields like DA, ML…… 02. Data: is any pieces of information represented in the form of text, image, numbers, sounds etc. 03. Data Analytics is a sub field in data science that focuses on utilizing data to draw meaningful insights and solving problem 04. Data Analysis: is a process in data analytics workflow. It is the application of statistics to derive a summary of data. 05. Business Intelligence: combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations to make more data-driven decisions.
  4. 4. DATA SCIENCE & ANALYTICS TERMS • 06. Machine Learning: is a sub field of AI and DS. The ability of machines to produce outcomes. It's all about implementing algorithms that lets machine receives data and uses the data to make prediction and identify patterns and give recommendation. ML cannot be implemented without data. Demand for real-time dashboards open opportunities for ML. • 07. Artificial Intelligence: is simulating human knowledge and decision making with computers. We reach ai through Machine Learning. • 08. Augmented Analytics: is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. We leverage augmented analytics to eliminate iterative processes like improve data quality, monitor data, prepare data and derive quick insights. www.datatechcon.com 4
  5. 5. 2 MAJOR CATEGORIES OF DATA SCIENCIST www.datatechcon.com 5 BUSINESS DATA SCIENTIST- FOCUS ON ANALYZING AND DERIVING INSIGHTS FROM HISTORIC DATA TO UNDERSTAND WHAT HAPPENED IN THE PAST….. PRODUCT DATA SCIENTIST– FOCUS ON APPLYING ML ALGORTHMS AND BUILDING MODELS THAT FORECAST FUTURE OUTCOMES……
  6. 6. HOW TO GET INTO DATA SCIENCE & ANALYTICS www.datatechcon.com 6 ACCREDITED DEGREE PROGRAMS ONLINE CERTIFICATION TRAINING OR BOOTCAMPS SELF STUDY
  7. 7. DATA CHALLENGES www.datatechcon.com 7 Poor Data Quality – Messy Data Inaccessible Data – Database Data Collection – lack of real-time data Data Silo Data Inconsistency – Disparate sources Data Management – SQL , NO SQL Cost – Tools, softwares, hardwares
  8. 8. This analysis historic data to uncover insights on past incidents such as revenue, sales, cost etc. Descriptive As the name suggests, predictive analytics is about predicting the future outcomes Predictive Prescriptive analytics determines which action to take to improve a situation or solve a problem. Prescriptive DATA ANALYTICS Data Analytics refers to process of analyzing raw data to uncover insights, identify trends & patterns to make informed business decision. Data is extracted from various sources and is cleaned and categorized to analyze different behavioral patterns. The techniques and the tools used vary according to the organization or individual. www.datatechcon.com 8
  9. 9. KEY BENEFITS OF DATA ANALYTICS www.datatechcon.com 9 Increase revenue 1 Mitigate risk of wasteful investment 2 Improve operational value 3 Monitor & Track KPIs towards goal projection 4 Automate reporting 5
  10. 10. WHEN TO USE ANALYTICS 50% 22% 28% www.datatechcon.com 10
  11. 11. DATA ANALYTICS TOOLS Most required tools and technologies based on research and job interviews SQL BI EXCEL PYTHON R www.datatechcon.com 1
  12. 12. Abilities to leverage technologies, tools or software HARD SKILLS Industry or functions or area of specialization DOMAIN EXPERTISE Communication, presentation, problem solving skills SOFT SKILLS DATA ANALYTICS SKILLS CATEGORIES The goal of becoming a successful data analytics professional is by having a combination skillsets of hard, soft and domain expertise. This will give you competitive edge over other applicants in a position or role. www.datatechcon.com 1
  13. 13. KPI METRICS KPI is also known as Key Performance Indicator. These are important metrics used to track and measure performance towards business goals. 01. Ecommerce: Revenue, Profit, Profit margin, ROI 02. Healthcare: Total Patients, response time, Recovery rate, Length of stay 03. Marketing: CTR, conversion rate, CPL, Customer LTV, ROAS, ROI www.datatechcon.com 13
  14. 14. To analyze patient’s health & Predict recovery cycle. HEALTHCARE To help detect customer that are likely to default in loans. FINANCE To increase brand awareness and customer conversion rate. MARKETING To forecast sales and customer’s lifetime value SALES To predict top performing products for restocking. LOGISTICS To identify fraudulent claims to help save cost. INSURANCE APPLICATION OF DATA ANALYTICS Succesfull companies harness the power of data and analytics to make data-driven decisions. www.datatechcon.com 14
  15. 15. DATA ANALYTICS JOB ROLES • Data Analyst • Business Data Analyst • Business Intelligence Analyst • Data Analytics Specialist • Data Visualization Engineer • Product Data Analyst • Marketing Analyst • Healthcare Data Analyst • Financial Analyst www.datatechcon.com 15 • Digital Marketing Analyst • Reporting Analyst • HR Analyst • Customer Insights Analyst • Web Analyst • CRM Data Analyst • Manager of Insights & Analytics • Analytics Manager • BI Analyst
  16. 16. Understand the purpose of the analysis BUSINESS GOAL Identify data sources and collect data DATA COLLECTION Manipulate and transform your dataset DATA CLEANSING Apply statistical analysis to dataset Analysis Create visualizations & Present the data Visualization DATA ANALYTICS FRAMEWORK Data analytics lifecycle workflow www.datatechcon.com 16
  17. 17. www.datatechcon.com 17 DATA SCIENCE DEVELOPMENT FRAMEWORK
  18. 18. DATA ANALYTICS CRISP-DM FRAMEWORK CRISP-DM stands for Cross Industry Standard Process for Data Mining (6 phases) • BUSINESS UNDERSTANDING – Understanding the business projects and objectives • DATA UNDERSTANDING – Identifying data sources and databases, collecting & exploring the datasets. • DATA PREPARATION – data preprocessing and data cleaning(the most time-consuming phase). • MODELING – Building model in using machine learning algorithms • EVALUATION – Evaluate the performance of the model • DEPLOYMENT – Deployment to production www.datatechcon.com 18
  19. 19. DATA ANALYTICS PROJECT QUESTIONS • Questions to ask when tasked with an analytics end to end project. • What is the goal of the project? • What is the main problem and solution goal? • What are the data sources and databases? • What data is available? • Is there a data dictionary? • What type of analysis is requires? Trend, exploratory, performance? • Who are the audience or end users? • How are the data related in each tables? www.datatechcon.com 19
  20. 20. DATA GOVERNANCE www.datatechcon.com 20 Data quality ensures we have quality data that is secured and have integrity. • Data quality • Data dictionary • Data analysts work within the plan
  21. 21. COMMON MISTAKES BEGINNERS MAKE www.datatechcon.com 21 Assuming it's easy - when u start skills its easy to assume that consolidating the data is easy. Records count - check the record count 50 states showing 100 Verify your calculations - don't trust the numbers. Use a calc Failing to ask for data dictionary - create one if it doesn’t exist Making assumption - don't assume ask questions Making calculations hard to use - document Joins - spend more time working with multiple tables to improve joins Limited access to the database - working with only csv or excel
  22. 22. LEARNING TAKEAWAYS • Learn how to leverage data & analytics to make data-driven decisions. • Understand data analytics workflow • Develop analytics interactive dashboards. • Learn how to uncover insights & tell a data story • Learn how to use core data analytics tools. • Become job ready for a data analytics roles DATA ANALYST LEARN MORE www.datatechcon.com 22
  23. 23. THANK YOUQuestions & Answers PERSONAL IG @omozara BUSINESS IG @datatechcon WEBSITE: www.datatechcon.com www.datatechcon.com 23

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