数据分析师必备技能清单

Ultimate Skill Checklist For Data Analyst

Contents

• Programming
• Statistic
• Mathematics
• Machine Learning
• Data Wrangling
• Communication and Data Visualization
• Data Intuition

Programming

• Python programming language
  • [ ] numpy
  • [ ] pandas
  • [ ] matplotlib
  • [ ] scipy
  • [ ] scikit-learn
• R programming language
  • [ ] ggplot2
  • [ ] dplyr
  • [ ] ggally
  • [ ] reshape2
• Optional
  • [ ] ipython
  • [x] ipython notebook
  • [ ] anaconda
  • [ ] ggplot
  • [ ] seaborn
  • [ ] Spreadsheet tools (like Excel)
• Additional Skills
  • [ ] Javascript and HTML for D3.js
    • [ ] D3.js
    • [ ] AJAX implementation
    • [ ] jQuery
  • [ ] C/C++ or Java

Statistic

• Descriptive and Inferential statistics
  • [x] Mean, median, mode
  • [ ] Data distributions
    • [ ] Standard normal
    • [ ] Exponential/Poisson
    • [ ] Binomial
    • [ ] Chi-square
  • [ ] Standard deviation and variance
  • [ ] Hypothesis testing
    • [ ] P-values
  • [ ] Test for significance
    • [ ] Z-test, t-test, Mann-Whitney U
    • [ ] Chi-squared and ANOVA testing
• Experimental design
  • [ ] A/B Testing
  • [ ] Controlling variables and choosing good control and testing groups
  • [ ] Sample Size and Power law
  • [ ] Hypothesis Testing, test hypothesis
  • [ ] Confidence level
  • [ ] SMART experiments: Specific, Measurable, Actionable, Realistic, Timely]

Mathematics

• [x] Translate numbers and concepts into a mathematical expression: 4 times the square-root of one-third of a gallon of water (expressed as g): 4 √(1/3 g)
• [x] Solve for missing values in Algebra equations: 14 = 2x + 29
• [ ] How does the 1/2 value change the shape of this graph?
• [ ] �Linear algebra and Calculus
• [ ] Matrix manipulations. Dot product is crucial to understand.
  �- [ ] Eigenvalues and eigenvectors -- Understand the significance of these two concepts
• [ ] Multivariable derivatives and integration in Calculus

Machine Learning

• Supervised Learning
  • [ ] Decision trees
  • [ ] Naive Bayes classification
  • [ ] Ordinary Least Squares regression
  • [ ] Logistic regression
  • [ ] Neural networks
  • [ ] Support vector machines
  • [ ] Ensemble methods
• Unsupervised Learning
  • [ ] Clustering Algorithms
  • [ ] Principal Component Analysis (PCA)
  • [ ] Singular Value Decomposition (SVD)
  • [ ] Independent Component Analysis (ICA)
• Reinforcement Learning
  • [ ] Qlearning
  • [ ] TD-Learning
  • [ ] Reinforcement Learning

Data Wrangling

• Python
  • [ ] Learn about Python String library for string manipulations
  • [ ] Parsing common file formats such as csv and xml files
  • [ ] Regular Expressions
  • [x] Mathematical transformations
    • [x] Convert non-normal distribution to normal with log-10 transformation
• Database systems (SQL-based and NO SQL based) - Databases act as a central hub to store information
• [ ] Relational databases such as PostgreSQL, mySQL, Netezza, Oracle, etc.
• [ ] Optional: Hadoop, Spark, MongoDB
• [x] SQL

Communication and Data Visualization

• [ ] Understand visual encoding and communicating what you want the audience to take away from your visualizations
• [ ] Programming
  • [ ] matplotlib
  • [ ] ggplot
  • [ ] d3.js
• [ ] Presenting data and convincing people with your data
  • [ ] Know the context of the business situation at hand with regards to your data
  • [ ] Make sure to think 5 steps ahead and predict what their questions will be and where your audience will challenge your assumptions and conclusions
  • [ ] Give out pre-reads to your presentations and have pre-alignment meetings with interested parties before the actual meeting