# Tag datascience

### NumPy-Functions

NumPy has many built-in functions and capabilities. We won’t cover them all but instead we will focus on some of the most important aspects of NumPy such as vectors, arrays, matrices, and number generation. Let’s start by discussing arrays.

NumPy arrays are the main way we will use NumPy throughout the course. NumPy arrays essentially come in two flavors: vectors and matrices. Vectors are strictly 1-d arrays and matrices are 2-d (but you should note a matrix can still have only one row or one column).

To know more about numpy function check the official documentation https://docs.scipy.org/doc/numpy/user/quickstart.html

Let’s begin our introduction by exploring how to create NumPy arrays. Please go through the jupyter notebook code. I have explained the code with comment, hope it will help you to understand the important functions of NumPy.

### Data Science – An Introduction

#### What is Data science?

It is a study that deals with the identification and extraction of meaningful information from data sources with the help of various scientific methods and algorithms. This helps in better decision making, promotional offers and predictive analytics for any business or organization.

#### What are the skills required to be a Data scientist?

• Programing Skill
• Python
• R
• Database Query Languages.
• Statistics and Probability
• BI Tools – Tableau, Power BI, Qlik Sense

#### Data Scientist VS  Data Analyst VS  Data Engineer

##### Data Analyst:

It is an entry-level job for those professionals who are interested in getting into a data-related job. Organisation expect from Data Analyst to understand data handling, modeling and reporting techniques along with a strong understanding of the business. A Data analyst required a good knowledge of visualization tools and database. There are two most popular and common tools used by the data analysts are SQL and Microsoft Excel.

It is necessary for the data analyst to have good presentation skills. This helps them to communicate the end results with the team and help them to reach proper solutions.

##### Data Engineer:

A Data Engineer specializes in preparing data for analytical usage. They have good idea about Data pipelining with performance optimization. A Data Engineer required strong technical background with the ability to create and integrate APIs. Data Engineering also involves the development of platforms and architectures for data processing.

So what skills required being a Data Engineer?

• Data Warehousing & ETL
• Machine learning concept knowledge
• In-depth knowledge of SQL/ database