Data analytics crash course
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Henrica Makulu is a data analyst and founder of HM Digital Training & Mentorship Program. She has 10 years of experience in digital literacy and 6 years in data analytics. In this presentation, she covers trends in data science and analytics, provides an overview of the data science process, and gives practical tips for getting started in data analytics through online courses and platforms like Kaggle, YouTube, LinkedIn, and Coursera. She emphasizes keeping the process simple and translating data into insights and action.
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Data analytics crash course
- 1. A crash course in data analytics WITH HENRICA MAKULU NUST APPLIED MATHS ALUMNI CASSAVA SMARTECH DATA ANALYST FOUNDER OF HM DIGITAL
- 2. 1. Industry Outlook 2. Datacate yourself! 3. Practical AGENDA
- 3. Henrica Makulu Data Analyst | Mentor | Founder Henrica is a tech professional with ten years experience in digital literacy and six years experience in data analytics. She loves to simplify tech concepts and help equip individuals and corporates with problem solving skills using technology BSc Hons. Applied Mathematics (National University of Science & Technology) 6 years experience in data analysis Has trained over 500 individuals on various skills including one on one training as well as corporate training using tools such as Microsoft Office, Qlik Sense, SPSS and Python. Trained Togo National Government Officials on Qlik Sense Dashboards. Trained South African Educators online on Digital Literacy in partnership with MANCOSA School of Education: https://youtu.be/Sr0ZupULp34 Founder of HM Digital Training & Mentorship Program Who am I? Recognised as one of Africas leading women in data analytics by TECHBUILD.AFRICA (2021) Data Science Certifications (WorldQuant University, Acadgild, IBM Digital Nation, LinkedIn Learning). Currently pursuing AZURE DATA SCIENTIST ASSOCIATE - MICROSOFT Part of Data Science Zimbabwe Community
- 4. 4 Trends Global & Local World Economic Forum named data science among the most competitive skills of the Fourth Industrial Revolution (Future of Jobs Survey & Report 2018) Africa is still behind and there is a lot of opportunity to be a pioneer in this. Definition Data analysis vs data science Data analysis involves examining large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data science, on the other hand, involves designing and constructing new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis. https://www.northeastern.edu/graduat e/blog/data-analytics-vs-data-science/ Data & Applications Academic / Personal / Corporate / Community Data science can be used to help make better decisions in your personal life, for your employer or in your community. Without data you're just another person with an opinion. Data Science/Analytics Landscape
- 5. DISRUPTION The world is moving. Think back 5, 10, 15 years. 4IR is here The future of careers is different: be future ready Self-service (e-commerce, airport check-in, vending machines etc) Artificial Intelligence (self driving cars, Google home, robots, drones etc) Big Data Cloud 5G Business powerhouses are big data users
- 6. Real time analytics! if you torture the data long enough it will confess
- 7. DATA DRIVEN DECISIONS From January 2020 to date we have managed to service 50 clients, almost half of whom are in the food industry. This is a slight decline (17%) from the 56 clients we served in 2019. Please find attached a detailed breakdown by industry 5 13 26 24 25 13 0% 20% 40% 60% 80% 100% 2019 2020 Manufacturing Food Clothing 2020: 50 17% YoY 2019: 56
- 8. 8 Kaggle For practical real life data science business problems and solutions: https://www.kaggl e.com/ (https://www.kaggle.com/guy baryosef/portfolio-analysis- preprocessing/notebook) 01 Youtube For specific how to videos 02 LinkedIn For influencers in your field and relevant expertise groups (Towards Data Science, Data Science Central etc) 03 Coursera Udemy DataFlair DataCamp (https://www.datacam p.com/community/tut orials/finance-python- trading) For online learning 04 Keep it simple. Datacate yourself
- 9. 9 Data Science Process - Overview Data Extraction Tools: SQL, Excel, Python Data Preparation Tools: Excel, Python Data Exploration Tools: Excel, Python Data Visualisation /Modeling Tools: Power BI, Qlik, Tableau Data Presentation Tools: PowerPoint Translating Data to Insights and Action. Requirement
- 11. SHAMELESS PLUG! Read my story: https://wtcafrica.wordpress.com/2021/04/23/i-love-data-story-telling-henrica-ketura-makulu/ Watch my Youtube vids: Follow me on LinkedIn: Henrica Makulu Check out my data articles on how to get into data science: resiliatech.com Check out my slide decks: slideshare.net
- 12. THANKYOU! EMAIL: HENRICAMAKULU@GMAIL.COM HENRICA MAKULU DATA SCIENCE & ANALYTICS HOW I LANDED A JOB WITH ZIMS BIGGEST TECH COMPANY ETC Check out my articles on LinkedIn and www.resiliatech.com I write about: