Future of Data Science and Artificial Intelligence

Data science is a process of using advanced analytics that focuses on analyzing the past or present data and eligible to predict the future outcomes. Data Science handles data analytics, artificial intelligence, Deep Learning, builds Machine Learning models and Big Data models.

In short, Data science is all about:

• - Analyzing the raw data
• - Limitless Potential Gaining Critical Insight
• - Visualizing the Data to get a better perspective
• - Understanding the data to get the final results

Benefits of Being Data Scientist

• Gain Good Communication Skill
• Mathematical and statistical knowledge
• Innumerable career opportunities
• Good for solving a complex business data problems

Career Opportunities of Data Science:

• - Data Scientist
• - Product Analyst
• - Machine Learning Engineer
• - Data Engineer
• - Business Analyst
• - Business Intelligence Manager
• - Artificial Intelligence Engineer

Tools for Data Science

Following are some tools required for data science:

• 1. Data Analysis tools: R, Python, Statistics, SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner.
• 2. Data Warehousing: ETL, SQL, Hadoop, Informatica/Talend, AWS Redshift
• 3. Data Visualization tools: R, Jupyter, Tableau, Cognos.
• 4. Machine learning tools: Spark, Mahout, Azure ML studio.

Process of Data Analytics:

• Requirement Understanding
• Data Preparations
• Data Explorations for Visualizations
• Decision Making: Test Data & Train Data
• Machine Learning

What does Data Analytics can do?

Descriptive Analytics: Data Analytics focused on Descriptive Analytics that is how we can analyze the Data of a Model. Concern of getting data analytics is to find out the type of Data i.e. Image, video, hybrid, descriptive, statistics, etc

Predictive Analytics:

It incorporates various statistical strategies from data mining, prescient demonstrating, and AI that investigate current and historical realities to make forecasts about future or generally unknown occasions.

Prescriptive Analytics:

Prescriptive Analytics is a type of business investigation which proposes choices for how to make the most of a future opportunity or moderate a future gamble, and shows the implications of every choice.

Difference between Data Science and Artificial Intelligence:

Data Science is broad chapter that includes the study of Artificial intelligence. As Artificial Intelligence is a part of Data Science.

Data Science

• - Detailed process that mainly involves pre-processing analysis, visualization and Prediction.
• - Data Science was developed to find out the hidden patterns and trends in Data.
• - Data Science can built complex models for extracting various facts.
• - Tools that are far more extensive than those used in AI.
• - By using the concept of data science, we can build complex models about statistics and facts about data.
• - Data uses the techniques of Data Analytics and Data Analysis.
• - The person who works in data science is known as data scientist.

Artificial Intelligence

• 1. It forecast the future events and trends.
• 2. Granting of autonomy to the data model.
• 3. Uses standardized data by embedded and vector methods
• 4. High levels of Complex
• 5. Uses a lot of Machine Learning Techniques.
• 6. Tools Used in AI: Tensor Flow, Caffe, Keras, etc
• 7. Career as an Artificial Intelligence Engineer