*START HERE*

Find what you already know, what you need, and the next steps

Here’s a quick summary of what you should read:

• Already Have The Skills: Read the Getting Hired, don’t need the others.

• Completely New: Start by reading the Data Analyst Section. You need to get your foot into a Data Analyst Role ASAP.

• Level Up From a Data Analyst: Just read the For MLE/Data Scientists section

Getting Hired

If you have the skillset already, read the posts below.
These posts should answer 90% of your hiring related questions.

1. Fixing your Resume

2. How to find even more jobs

3. Prepping for your interview

4. How To Become a Data Scientist

5. Make $300k in 3 years

6. How to work 2 full time jobs

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For Data Analysts

*Completely New*

If you are completely new to the field, and have not acquired R/Python, this is your starting platform

1. Setting up R & Python 1

2. Setting up R & Python 2

3. Variables, Comments, and Interpreting Errors

4. Strings 1

5. Strings 2

6. Immutability & String Functions

7. Loops, Functions & Scope

8. Conditional Expressions

9. Libraries, Modules & Packages

Python

1. Data Structure - Tuples

2. Data Structure - Lists

3. Data Structure - Dictionaries

4. Classes and Instances

5. Object Inheritence

R Programming

1. Data Structure - Vectors

2. Data Structure - Matrix

Congrats, even if things don’t work out, you are now qualified for a simple dev job using Python/R

Structured Query Language

Aah, good old SQL, the lifeblood of all data professionals. You’ll be heavily grilled for your SQL knowledge. Be prepared

1. Basic Terminology

2. Your First SQL Query

3. Filtering Data

4. Aggregating Data

5. Insert and Update Data

6. Create, Delete, and Drop Data

7. SQL Keys, and Joins

8. Data Manipulation (Basically Data Skills in SQL)

9. Subqueries

10. Views

11. Fetching the Data

12. Finale - Current Meta

Don’t know Linear Algebra

If you already know how to code, but don’t know any linear algebra, then start here:

1. Vectors

2. Linear Independence, Basis, and Spans

3. Products, Lengths and Orthogonal

4. Matrices

At this point, you should be able to read basic data wrangling techniques confidently and with reasonably proficiency

Don’t know Statistics

You have now acquired basic programming, and lin alg, and are ready to progress to basic statistics here:

1. The 5 Number Summary

2. Probability Fundamentals

3. Probability Distributions

At this point, you should be able to read most research papers without too much trouble

Linear Regression (Linear Modelling)

At this point, you can start to learn about your first modelling technique called Linear Regression, start here:

1. Understanding Linear Regression

2. MLE Cheatsheet - Lin Reg

3. Quadratic Regression

4. Stepwise Regression

5. ANOVA Regression

6. Kernel Regression

7. Principal Components Regression

8. Bayesian Linear Regression

9. Log Linear Models

Having completed the first two posts, If you understand what’s going on in the first 2 posts, and understand some basic SQL queries, you now have the skill set for extremely basic data analytics jobs.

Classification

If you have already had some exposure to regression analysis, here are some algorithms examples you can look at that deal with classification problems:

1. Logistic Regression - this is classification, don’t let the title fool you.

Statistical Biases

Here are some statistical biases you will see in your dataset, and in what domain you will encounter them in. Additionally, a quick solution as to how to keep your analysis clean, or remove the bias is in the article as well.

1. Look-ahead Bias - a bias related to time series analysis.

2. Backfill Bias - a bias everyone analyzing stocks and portfolios will encounter.

3. Allocation Bias - for those that work in the medical industry.

4. Verification Bias - for those working with customer data

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For MLEs/Data Scientists

Only read this section here, if you’ve already got some relevant data work experience. A certificate, or an online course doesn’t count. We only value your work experience if it was done in the real world.

Python Data Skills

1. Handling CSV reading problems

2. Handling Excel reading problems

3. Handling API (JSON) reading problems

4. Handling HTML reading problems

5. Inspecting Your Data

6. Anomalies Part 1

7. Anomalies Part 2

8. Data Visualization

9. Series

10. Data Cleansing Basics

11. Data Cleansing with Aggregations

12. Combining DataFrames