Data Science is one of the most promising and in-demand career paths in future *trends*. Data scientists are aware that they must be mastering the traditional skills of analyzing big data, data mining, and programming skills. They must be mastering the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

## Data Science Life Cycle

Data Science Life Cycle containing five stages which are **Capture**, (data acquisition, data entry, signal reception, and data extraction) **Maintain**, (data warehousing, data cleansing, data staging, data processing, data architecture) **Process**, (data mining, clustering, data modeling, data summarization) **Analyze**, (exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis) **Communicate**, (data report, data visualization, business intelligence, and decision making).

## What does Data Scienctist do?

Simply, Data scientists are the ones who solved complicated data problems with their strong expertise in certain scientific disciplines. They work with several elements related to mathematics, statistic, computer science, etc. These professional are a data-driven individual who is capable of building complex algorithms to create information that helps the company to drive the strategy for the company itself. They use the latest technologies for finding solutions that are crucial for company growth and development. Data Scientists present the data in a more useful form compared to the raw data available from structured or unstructured forms.

## What kind of skill that Data Science need?

Besides programming skills, to be an efficient data scientist you also need some various skill-sets and competencies such as:

- You must be
**very innovative**kind of person in applying various techniques to extract data and get useful insight from it - Posses a hand-on experience in
**data mining techniques**such pattern detection, decision trees, clustering or statistical analysis - Able to
**locate and construct**rich data sources **Research and Analyzing**data to create a useful insight**Data Visualization**that will help others to understanding what youâ€™ve found**Leadership and Communication**that help you collaborate with other divison like Business Analyst, Engineers, and Product Managers

Data Scientist should be very strong in Programming, Statistics, Mathematics, Business skills and working knowledge of the related skill-sets.

## How do I become a data science?

In this topic, we will recommend you to study it thoroughly to provide the minimum background needed to start doing data science. Here is the following list for you to study.

### 1. **Multivariable Calculus**

Multivariable calculus is very important for you to build a machine learning model that will help you to be a better data scientist. You must be familiar with :

- Functions of variables
- Derivatives and gradients
- Step function, Sigmoid function, Logit function
- Cost function
- Plotting of functions
- Minimum and Maximum function values

### 2. **Linear Algebra**

Linear algebra is used for processing data, transformation data, and evaluation models. The topics you need to be familiar with is :

- Vectors
- Matrices
- Transpose Matrix
- Inverse Matrix
- Determinant Matrix
- Dot product
- Eigenvalues
- Eigenvectors

### 3. **Optimization Methods**

Being able to learn the weights that must be applied to test the data for forecasting, you must be familiar with :

- Cost function/Objective function
- Likelihood function
- Error function
- Gradient Descent Algorithm

### 4. Basic Programming

You may decide to focus on one programming language like R or Python for a beginner, both are considered as a top programming language for data science. You should acquire this basic skill:

- Basic R syntax
- Foundational R, for instance data types, arithmetics vectors, indexing, and last but not least data frames
- Sorting, data wrangling, and data visualization
- Jupyter notebooks
- Be familiar with Python libraries.
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