1. Role Vs Task Vs Skill
1.1. Role
1.1.1. Data Analyst
1.1.1.1. Analyzing data in order to tell a story and produce actionable insights
1.1.2. Machine Learning Enginnering
1.1.2.1. Working software and consists of other software components that run autonomosly with minimal human supervision
1.2. Task
1.2.1. Regression
1.2.1.1. Exp: Regression trees, Linear regression
1.2.2. Classification
1.2.2.1. Exp: Decision trees, Support vector machine
1.2.3. Clustering
1.2.3.1. Exp: Mean-shift(High accuracy), Hierarchical clustering
1.2.4. Multivariate Querying
1.2.4.1. Exp: Nearest neighbors, Range Search
1.2.5. Density Estimation
1.2.5.1. Exp: Kernel density estimation(High accuracy), Mixture of Gaussians
1.2.6. Dimension Reduction
1.2.6.1. Exp: Manifold learning/KPCA(High accuracy), Principal component analysis
1.2.7. Testing and Matching
1.2.7.1. Exp: Minimum spanning tree
1.3. Skill
1.3.1. Computer Science Fundamental and Programming
1.3.1.1. Exp: Data Structures(stacks, queues),Python/C++/R/Java, algorithms(Searching, Sorting)
1.3.2. Probability and Statistics
1.3.2.1. Exp: conditional probability, Bayer rule, distributions(Normal, Binomial, Poisson), analysis methods(ANOVA, hypothesis testing), measures(Mean, Median, Variance)
1.3.3. Data Modeling and Evaluation
1.3.3.1. Exp: correlations, clusters, eigenvectors, classification, regression, anomaly detection
1.3.4. Applying Machine Learning Algorithms and Libraries
1.3.4.1. Exp: libraries/packages/APIs(e.g. scikit-learn, Theano, Spark MLib, H2O, TensorFlow)
1.3.5. Software Engineering and System Design
1.3.5.1. Exp: analysis, system design, modularity, version control, testig, documentaion
2. Technical skills
2.1. Computer Science's skills
2.1.1. SQL Database – able to write and execute complex queries in SQL
2.2. Anaylytics skill
2.2.1. R – analytics tool
2.3. Programming experience
2.3.1. Python, Java, R
2.4. Statistics and Probability
2.4.1. Prior knowledge in this would help in problem solving
2.5. Basic Linear Algebra
2.5.1. Regression analysis
3. Self-learning resources
3.1. Online courses
3.1.1. Coursera,Udacity, Khan Academy
3.2. Kaggle
3.2.1. Competitions - World most elite machine learning leaderboard Datasets - Analyze public datasets Kernels - Run code in cloud and receive community feedback
3.3. MOOC
3.3.1. Codecademy
3.4. Video Tutorial
3.4.1. Youtube
4. Soft skills
4.1. Intellectual curiosity
4.1.1. A data scientist should invest time and energy in getting into machine learning knowledge
4.2. Communication skills
4.2.1. Needed to translate technical finding to non-technical team such as Marketing departments
4.3. Business acumen
4.3.1. Understanding in industry and company on how to make profit and effective to strategy execution