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Data by Mind Map: Data

1. Based on Data & Technology

1.1. Data

1.1.1. Descriptive analysis What happen in the past?

1.1.2. Predictive analysis What will happen in the future?

1.1.3. Prescriptive analysis How we can change the future?

1.2. Maps of relation

1.2.1. Basic Technology at the center Business Communication Artificial inteligence

1.2.2. Data

1.2.3. Data science

1.2.4. Machine Learning applied to Big Data

1.3. Concepts

1.3.1. Artificial Intelligence Components Machine Learning Natural Learning Processing NLP Speech Expert systemas Planing, schedulin and optimization Robotics Vision Vens 1 2 Process Amadeus Analytics vs AI 1 3 stages Machine learning Machine intelligence Machine consiousness History

1.3.2. Data Science Data Sciences Statistics Econometrics Machine Learning Data Mining Artificial inteligence Operations Research Natural Language Processing Additional Methods and techniques Linear / No linear programming MCMC methods Latent class methods Structural equation models Discrete choice models Dimensionality reduction Hierarchical Bayes Models Techniques Linear / No linear regressions Logistics regressions Time-series models Optimization A/B testing Clustering Factor analysis Principal component analysis Neural networks Support vector machines Bayesian Techniques Survival Analysis Tools R, SAS Python, Java, C++ SPSS, Matlab, Minitab CPLEX, GAMS, Gauss Tableau, Spotfire VBA, Excel Javascript, Perl, PHP Open Source Databases MySQL AWS, Cloud Solutions Vertical applications Big Data Solutions Social Media Analytics Online advertising Display marketing Text analytics Retail analytics Customer analytics Forecasting Pricing and revenue oprtimization Custom insights Custom reporting Custom dashboard Data adapters Social Data connectors ( facebook, twitter ) Extract - transfer - load ETL toolset Outreach / Hooks Hooks into agent app Hooks into CRM platforms Hooks into mobile devices

1.3.3. Big Data Ecosystem 1. Data Creation ( producer ) Machine and sensors Transaction and usage logs Relationships and social influence Mobile apps data Email & messaging Goelocation 2. data acquisition (architects & engineers) Shared nothing scale-out storage + SSD MPP + In-memory compute Converged infrastructure Non-relational DWH High speed resiliency networking Cloud Hadoop 3. Info processing (scientist & analyst) 4. Business process (en user)

1.4. People

1.4.1. Data Scientist (team?) What is? Still a fuzzy concept Battle of the Data Science Venn Diagrams The process Mindset Scientific mindset Curious Creative Business thinking Pragmatic Skills Computer science Analytics Data Management Art & Design Entrepreneurship Educational background Steps to become Data Scientis Step 1. Get Good at Stats, Maths and Machine Learning Step 2. Learn to Code Step 3. Understand Databases Step 4. Explore the data science workflow Step 5. Level up with big data Step 6. Grow, connect and learn Step7. Immerse yourself completely Step 8. Engage with community Profile Woman Man Data Science learning plan for 2017 Data Science learning plan for 2017 Glossary Simple Beginner’s guide Infographics A Step-by-Step Guide to learn Advanced Tableau Learning plan for beginners Learning Plan 2017 for Transitioners Learning Plan 2017 for Intermediates Data Scientist vs Data Engineer vs Statistician Job Roles in Data Science Industry Hackathons

1.5. Google trends

1.5.1. Last years

1.6. Process

1.6.1. 1