Learn data science and you could find yourself working in this promising, well-compensated field.

There’s no doubt about it: data scientists are in high demand. As of 2020, the average data scientist in the US makes over $113,000 a year, and data scientists in San Francisco make over $140,000.

Learn data science and you could find yourself working in this promising, well-compensated field.

With no doubt, data scientists are in significant demand. As of 2021, Learning data science is promising, as well as a profitable field.

Although you may not be interested in becoming a data scientist, yet learning data skills and upgrading your data ability can become an advantage in your current career.

Data continues to alter our working abilities, the way we interact with others, and just about influence every dimension of our routine lives. For instance, eCommerce is seeping into our functioning.

With the escalation of websites like Myntra, Flipkart, Amazon, Snapdeal, and so forth, customer personalization or customization has become a standard.

Not only so that the customer‘s view timing increases but also in hopes that he purchases the next recommended product.

These new possibilities are credited to Data Science and Machine Learning.

These machines purposefully endorse related or similar items based on purchases bought by other customers, who had purchased similar items together.

Data-driven decision making is the main concept here.

## A Systematic Approach

### Adept a Programming Language

Prior to the odyssey of mastering data science and machine learning, one must acquire knowledge of a language. Python, C, Julia, R is recommended as a beginner’s tool.

A language is a basis by which the learner can understand the workings of algorithms.

Here, Python is mentioned as a recommendation, as its libraries can be implemented to machine-learning algorithms.

The following points should be maintained while learning:

- Absorb the key fundamentals: get equipped with coding in a peculiar IDE like Jupyter or Pycharm.
- Exercise python projects and decode problems by applying basic concepts.
- Pick up Web Scraping with Python: It enables you to learn how to accumulate data and use it to your advantage.

## Understand Data Science Algorithms and Statistics

Proficiency in statistics and data science basic algorithms is essential.

The learner must know how to distinguish between classification, regression, and clustering obstacles as with this he can build a distinct data science model. In accordance with the type of problem he experiences, the understanding of these three machine learning techniques helps to yield benefits.

Predominantly, statistical analysis defines a pattern to disorganized variables and information. Some requisites of statistics needed for Data Science are as follows:

- Bayesian Thinking and modelling: “
*Bayesian philosophy is based on the idea that more may be known about a physical situation than is contained in the data from a single experiment.**Bayesian methods can be used to combine results from different experiments, for example. In other situations, there may be sound reasons, based on physics, to restrict the allowable values that can be assigned to a parameter.”* - Regression Analysis modelling: “
*Regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the ‘outcome variable’) and one or more independent variable (often called ‘predictors’, ‘covariates’, or ‘features’).”* - Statistical Methods for Machine Learning: “
*Statistical learning theory is the broad framework for studying the concept of inference in both supervised and**unsupervised machine learning.*” - Statistical hypothesis testing: “
*A statistical is a hypothesis that is testable on the basis of observed data modelled as the realised values taken by a collection of random*” - Probability and Statistics: “
*Probability deals with predicting the likelihood of future events, while statistics involves the analysis of the frequency of past events.*”

## Some links of online courses you could use:

- Bayesian Statistics: From Concept to Data Analysis | Coursera
- Regression Models | Coursera
- Data Science: Statistics and Machine Learning | Coursera
- Hypothesis Testing, An Introduction | Coursera

## Construct a personalized Syllabus

The mission to expertise Data Science is a tough challenge but will render high results. A rigid course plan must be established.

Yet enabling the plausibility of being flexible enough to update the course with the knowledge of current trends.

Certified Courses and Free online tutorials, integrated into a scheduled timetable for the course of action must be followed.

**Another reason for you to stay motivated**: Some job titles related to Data Science are Data Architect, Data Engineers, Business Analyst Specialist, Business Intelligence Engineer, etc are in high demand and on a high pay grade.

And, since Data Science is here to stay, the need for experts in this field will only trend.

## Connect with Groups & Communities

There are a plethora of free online groups and communities.

Online communities can be defined as a virtual platform where a group of people individuals with comment interest in a particular subject. In such group-settings, participants connect and share resources for projects. It would also be used as a doubt solving medium or to gain members to your team.

## Begin to work towards the goal

Analyze and assess current projects by examining the code from start to finish. Good theoretical knowledge is never enough, practical implementation of the same is highly praised.

To understand better start with a moderate project that would require basic knowledge and relevant skills. Online websites provide free projects along with their Source Code. Some reliable links are given below:

- Data Science Projects with Source Code to Strengthen
- Data Science Projects with Source Code to kick-start

The wide-scale integration of Data Science has begun. Although it may be invisible to the common man, it contributes towards his digital mark. The potential rise in data analyzing power is fueling advances in the technological industry.

The impact of which can be seen not only in commerce but also in medical research, automation and the economy.

Therefore, it is impossible to limit the rising trend of Data Science for a time period. Data Science is here to stay.