what is Data Science and salary

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 What is Data science ?

          Data scientist is a professional who is skilled in extracting meaningful insights and knowledge from data through the analysis of massive amounts of data . This analysis help data scientists to ask and answer questions like what happen , what will happen , why it happened and what can be done with the results.

Data science is all about using various techniques , algorithms , to analyse large amount of dataset to extract meaningful data and applying them in various business domains .

why is Data Science Important ?

                   Data Science  is  important because it  combine  tools , methods , and technology  to  generate  meaning   from  data .  Data science is one of the fastest growing technology in the world . Data science is a high-paying field that offers many jobs and opportunity .  

Data Scientist Skills:

      1. Mathematics

      2. Probability

      3. Statistics

      4. Programming

      5. Machine Learning

      6. Deep Learning

      7. Data visualization Tools

1. Mathematics :

           Maths is a core of data scientist . If you're mathematician you can very easily become a programmer . Data Science careers require mathematical study because machine learning algorithm and performing analyses from data require maths . while maths not be the only requirement for your career path in data science but its often one of the most important . The fundamental pillars of mathematics that you will use daily us a data analyst is linear algebra , statistic and probability . Mathematical calculation are an essential part of most python development . 

2. Probability:

                       Probability is one of the fundamental  elements  of  statistics that is  equal to  the  number of  desired outcomes(x)  divided  by  the  total number  of  possible  outcomes(T) . Probability  allows  data scientists  to assess the  certainty  of  outcomes of a particular study or experiment.

3. Statistics:

               In data science, statistics is at the core of  sophisticated  machine learning algorithms, capturing  and translating  data   patterns   into actionable evidence . Data  scientists use  statistics  to gather ,  review , analyze , and draw conclusions from data , as well as  apply quantified mathematical models to appropriate variables. Data scientists work  as programmers, researchers, business executives , and more. However, what all of  these  areas  have  in common is a  basics  of  statistics .  Thus , statistics  in  data science  is  as  necessary  as understanding programming languages.

4. Programming:

             While there are a large quantity of useful languages you can learn, these two languages were the top data science programming languages in 2023.

python was the most popular data science programming language of 2023, and the reasons why are endless.

It is easy to use, and easy to learn. Python provides all the necessary tools for the 4 steps of problem solving-

Data collection & cleaning , data exploration, data modeling and data visualization.

python also has a number of advanced deep learning libraries which makes it the default language for artificial intelligence. The versatility of Python makes it the key factor in it being the most popular language for data science.

 Java is another very popular language among data scientists. It is one of the most tested and proven languages. Java makes application scaling a much easier process which makes it a great choice for building large artificial intelligence and machine learning applications.

Java can be very versatile due to popular languages like scala being a part of the Java Virtual Machine ecosystem. The JVM ecosystem is a great reason for aspiring data scientists to learn Java because it provides an easy entry path to many more useful data science languages.

5.Machine Learning:

             Machine learning and data science  can  work  hand  in  hand . Take  into  consideration  the definition of  machine  learning  -  the  ability  of  a  machine  to generalize knowledge from data. Without data  , there is very little  that  machines  can learn .  If anything , the  increase  in usage  of machine learning in many industry will act as a catalyst to push data science to increase relevance. Machine learning is only as good as the data it is given and the ability of algorithms to consume it . Going forward, basic levels of machine learning  will  become a  standard requirement for data scientists.