An overview of Data Science & Job Roles

Data Scientists jobs are demanded 400% more over the last 1 year.  Data skills are value added while combining with other skills such as domain, project, and business skills. 



Data is the information, and science is for experiments. So, conducting experiments on data to deliver the results will be our accountability in data science. It is an interdisciplinary field that uses the following: 
  1. Scientific Methods 
  2. Processes
  3. Algorithms
  4. Systems 
Data are the new oil. For data, the knowledge is extracted as energy is extracted from oil. Especially, data is considered as the new oil in deep learning. Learning algorithms are refineries that extract information from raw data. 

To extract the data, three combination of skills is required. If you are skilled in one of the below skills, bridging the gap of others skills over the time will master us in Data Science. 
  • Programming
  • Mathematics 
  • Domain 

Programming

There are three languages which majorly used in Data Science. 
  • Python  
  • R 
  • SAS 



Python is extremely fast whereas SAS is relatively slower. Python is bundled with all the packages installed, and it is another advantage of choosing Python. We can't use R and SAS for Artificial Intelligence, but Python is used for Data Science, Machine Learning, and AI as well. R is primarily used for statistical learning, since statistician uses  R, R is considered the programming language for Data Science. 

Mathematics 

  1. Analytics 
  2. Artificial Intelligence 
  3. Machine Learning 



            Statistics                                    -   Data Analytics                                 
            Probability & Linear Algebra -   Machine Learning                          
            Calculus                                     -   Artificial Intelligence                     

The topics which cover the Data Science would be: 

Fundamentals 

Basics Python ( Inlcuding Numpy & Pandas)
Statistics Basics

Data Analysis 

Data Manipulation 
  1. Matplotlib
  2. Seaborn    
Exploratory Data Analysis 

Machine Learning 

Fundamentals 
Basic Machine Learning algorithms
Advanced Machine Learning algorithms 

AI Tools 

Tensorflow 
Keras 

Computer Vision/NLP 

Deep Learning 
CNN/RNN 
Object Detection 

AI and Machine Learning are much more than fancy Data Analytics. Because, Data Analytics has happened, Machine Learning will happen, and AI also will happen. 

Jobs & Roles 

  • Data Analyst Jobs are preferable for less than 3 years experienced candidates who are good at programming.
  • Business Analyst Jobs are preferred for 3 to 7 years experienced candidates. These BAs are accountable for leadership, bring best practices from other company, and own value to the data. 
  • Data Scientist are who are good at programming, mathematics, and data.
  • Data Engineers are who build the scalable Big Data ecosystems, and this role is similar to ML Engineer or Data Architect, but not an entry-level job. 
  • Data Architects are expertise in Data warehousing, Modelling and ETL. 
  • Data and Analytics Manager should have excellent leadership skills and good at Data Science technologies. 
  • Machine Learning Engineers are good at programming and strong grasp in statistics and mathematics. Those who have years of experience in Data Science and software engineering, as well as advanced college degree can apply for this. 
  • AI Analyst to AI Engineer to AI Scientist to AI PM to Head of AI to AI Leader are the hierarchy of Job Titles.  
  • AI Engineer builds the AI model using ML algorithms and neural networks and preferably have 5 years experience. 

 Note: AI is not robotics, but AI is being intelligent and taking decisions. 


References: Google

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