Data science involves the systematic examination of data in order to extract valuable insights that can inform business decision-making and strategic planning.

Data science combines Math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. 

COMPUTER SCIENCE FOR DATA SCIENCE

Data science borrows heavily from Computer Science in the following aspects;

programming skills using programming languages such as python (dominantly), R, SQL(PostgreSQL), NoSQL(MongoDB, Cassandra) and Big Data Technologies. 

Knowledge of software development (HTML, CSS, JavaScript, React.js) and other programming languages and frameworks(e.g. Java, Springboot)  is highly recommended.  This is beyond this course and will not be covered.

You will handle and clean small datasets using spreadsheets.

You will clean large datasets using SQL, Python and R.

You will use big data technologies such as Hadoop to gain more insight from data;

You will have a hands-on experience in using DataViz tools such as Power BI and Tableau to visualize your data by creating reports and dashboards

You will build machine and deep learning models using modules and libraries such as sklearn, TensorFlow, keras etc. using python.

You will get experienced in cloud deployment using Amazon Web Service, AWS.

STATISTICS AND MATHEMATICS FOR DATA SCIENCE

Data science borrows heavily from Mathematics and Statistics in the following aspects:Mathematics:

Calculus,

Principal Component Analysis

Linear Algebra

Statistics:

Probability, Random variables and Probability distribution functions (PDFs)

Mean, Variance, Standard Deviation and Covariance and Correlation

Bayes Theorem, Linear Regression, Ordinary Least Squares (OLS) and Gauss-Markov Theorem

Parameter properties (Bias, Consistency, Efficiency), Confidence intervals and Hypothesis testing

Statistical significance, Type I & Type II Errors and Statistical tests (Student's t-test, F-test)

p-value and its limitations, Inferential Statistics and Central Limit Theorem & Law of Large Numbers

BUSINESS DOMAIN AND DATA ASCIENCE

Understanding the business domain is essential for data scientists. They work across various industries, using data to extract insights for businesses.

This aspect demands soft skills and a thorough comprehension of the industry at hand.

Healthcare: Data scientists in healthcare may analyze patient records to identify trends in diseases or develop predictive models for patient outcomes.

Finance: In finance, data scientists might build algorithms to detect fraudulent transactions or analyze market data to inform investment decisions.

E-commerce: Data scientists in e-commerce could work on recommendation systems to personalize product suggestions for customers or optimize pricing strategies based on consumer behavior.

Example roles in the field of data science

Data Analyst

Data Scientist

Data Engineer

Machine Learning Engineer

Business Intelligence Analyst

Data Architect

Statistician

Database Administrator

Data Visualization Specialist

Quantitative Analyst