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# Nick Singh Data Science

The first step in learning machine learning is to become familiar with its three main forms.

Computers learn to act on their own, we no longer need to write detailed instructions for performing certain tasks. Therefore, machine learning is of great importance for almost any field, but above all it will work well where there is Data Science.

### Five basic steps in working with data

- Collection. Search for channels where you can collect data, and the choice of methods for obtaining them.
- Checking. Validation, leveling of anomalies that do not affect the result and interfere with further analysis.
- Analysis. Examining data, confirming assumptions.
- Visualization. Presentation of information in an understandable form: graphs, diagrams.
- Reaction. Data-driven decision making. For example, changing the marketing strategy, increasing the company's budget.
- The path to this profession is difficult: it is impossible to master all the instruments in a month or even a year. You have to constantly learn, take small steps every day, make mistakes and try again.

## Step 1. Statistics, mathematics, linear algebra

For a serious understanding of Data Science, you will need a fundamental course in probability theory (mathematical analysis as a necessary tool in probability theory), linear algebra, and mathematical statistics.

Fundamental mathematical knowledge is essential to analyze the results of applying data processing algorithms. There are strong engineers in machine learning without such education, but this is rather an exception.

### What to read

**Nick Singh Data Science** book "**Ace the data science interview: 201 real interview questions asked by FAANG, tech startups, & Wall Street**" (2021).

Elements of Statistical Learning by Trevor Hasti, Robert Tibshirani and Jerome Friedman - if there are many gaps left after graduation. The classic sections of machine learning are presented in terms of mathematical statistics with rigorous mathematical calculations.

**Deep Learning **by Ian Goodfellow. The best book on the mathematical principles behind neural networks.

**Neural Networks and Deep Learning** by Michael Nielsen. To familiarize yourself with the basic principles.

**The Complete Guide to Mathematics and Statistics for Data Science**. A cool and fun step-by-step guide to help you navigate math and statistics.

**An introduction to statistics for Data Science** will help you understand the central limit theorem. It covers populations, samples and their distribution, and contains useful videos.

**The Complete Beginner's Guide to Linear Algebra for Data Scientists**. Everything you need to know about linear algebra.

**Linear Algebra for Data Scientists**. An interesting article introducing the basics of linear algebra.

## Step 2. Programming

Familiarity with the basics of programming will be a big advantage. You can make it a little easier for yourself: start learning one language and focus on all the nuances of its syntax.

Pay attention to Python when choosing a language. First, it is ideal for beginners, and its syntax is relatively simple. Secondly, Python is multifunctional and in demand in the labor market.

### What to read

Automating Routine Tasks with Python: A Practical Beginner's Guide. A practical guide for those learning from scratch. It is enough to read the chapter "Manipulating strings" and complete the practical tasks from it.

**Codecademy** - Learn some good general syntax here.

**An easy way to learn Python 3 **is a brilliant manual that explains the basics.

**Dataquest** will help you master the syntax.

**The Python Tutorial **is the official documentation.