#### Discover more from Data Science & Machine Learning 101

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.

# For Data Analysts

## *Completely New*

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

### Python

### R Programming

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

## Don’t know Linear Algebra

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

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:

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:

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:

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

## 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.

- a bias related to time series analysis.**Look-ahead Bias**- a bias everyone analyzing stocks and portfolios will encounter.**Backfill Bias**- for those that work in the medical industry.**Allocation Bias**- for those working with customer data**Verification Bias**

# 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.

## *START HERE*

think you shoud add data wrangling to this page