Data science generally refers to the process of working out insights from large datasets of unstructured data. This means using predicative analytics, statistics and machine learning to wade through the mass of data.

The practice of data science, may seem to have started in the last decade, but it has been with us for a while longer. Data science began in statistics. In recent years, its popularity has grown considerably due to innovations in data collection, technology, and mass production of data worldwide. Part of the evolution of data science from statistics is the inclusion of concepts such as machine learning and artificial intelligence.

In this article, we’ll share a concise summary of the concepts of which a beginner needs to get started in data science. Let me highlight them before we get started:

1. Data Scientist Role and Responsibilities
2. Python for Data Science
3. Data Visualization
4. SQL for Data Science
5. Statistics for Data Science
6. Data Exploration
7. Machine Learning for Data Science
8. Model Deployment

1. Data Scientist Role and Responsibilities

For you to be a data scientist you must first definitely know the roles and responsibilities of data scientists. How they design data modeling processes, create algorithms and predictive models to extract the data the business needs, and help analyze the data and share insights with peers. You must know the process for gathering and analyzing data, For Instance:

• Capture the data
• Process the data
• Analyze the data
• Communicate the results
• Maintain the data

2. Python for Data Science

Python is a programming language that is easy to learn. Its syntax is easy and code is very readable. Python has a lot of applications. It's used for developing web applications, data science, rapid application development, and so on. Python allows you to write programs in fewer lines of code than most of the programming languages thus its popularity grew rapidly in Data Science.

Python also has tools and processes used by data scientists to format, process, and query their data. They include:

• Pandas - Pandas provides fast, flexible, and expressive data structures to make working with relational or labeled data more intuitive. It performs well with tabular type of data (such as SQL tables or Excel spreadsheets) and is really good with time-series data (like, say, temperatures taken on a hourly basis).
• NumPy - NumPy adds big data-manipulation tools to Python such as large-array manipulation and high-level mathematical functions for data science. NumPy is best at handling basic numerical computation such as means, averages, and so on. It also excels at the creation and manipulation of multidimensional arrays known as tensors or matrices.
• Matplotlib - It provides a Python object-oriented API for embedded plots into applications using general-purpose GUI interfaces. You can make elaborate and professional-looking graphs, and even build “live” graphs that update while your application is running.
• Seaborn - Seaborn is a python graphic library built on top of matplotlib. It allows to make your charts prettier with less code.
• Regular Expressions - Regular expressions are huge time-savers for programmers. They allow you to specify a pattern of text to search for or filter.

3. Data Visualization

Data visualization is the representation of data through use of common graphics, such as charts, plots, info-graphics, and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.
Their are different Data Visualization Tools in data science. We have already explored data visualization python tools such as Matplotlib and Seaborn in above section. Their are other data visualization tools that do not require coding, for instance:

• Tableau (https://www.tableau.com/) - Based on Dashboards which act as a data visualization tool where users can easily analyze trends and statistics. It can be a powerful way of communicating results of a Data Science project.
• Infogram (https://infogram.com/app/) - Web-based visualization environment; infographic environment. Multiple PDF/PNG or HTML-based templates;
• Flourish (https://flourish.studio/examples/) - Another web-based visualization environment.
• Datawrapper (https://www.datawrapper.de/) - Web-based visualization and map creation environment.

4. SQL for Data Science

SQL (Structured Query Language) is a database computer language designed for managing data in relational database. A database is an organized collection of data stored and accessed electronically. Small databases can be stored on a file system, while large databases are hosted on computer clusters or cloud storage. There are lots of different database systems, for instance: MySQL, Oracle Database, Microsoft SQL Server, IBM DB2, Sybase, PostgreSQL, etc.

In Data Science, SQL is used for performing various operations on the data stored in the databases like updating records, deleting records, creating and modifying tables, views, etc. SQL is also the standard for the current big data platforms that use SQL as their key API for their relational databases.

5. Statistics for Data Science

Having Statistics knowledge in data science will make it easy for you to make insight in data. Statistics provides you with the tools and methods to perform data analysis. Its important to learn the following statistics concepts:

• Mean, Variance, Standard Deviation
• Random variables
• Probability Theory
• Descriptive Statistics
• Bayes Theorem
• Linear Regression
• Gauss-Markov Theorem
• Over- and Under-Sampling
• Confidence intervals
• Statistical significance
• Inferential Statistics
• Central Limit Theorem & Law of Large Numbers

6. Data Exploration

Data exploration is the initial step in data analysis, where users explore a large data set in an unstructured way to uncover initial patterns, characteristics, and points of interest. Exploration Data analysis(EDA) is important because it helps in:

• Spotting mistakes and missing data.
• Checking of assumptions.
• Selection of appropriate models.
• Mapping out the underlying structure of the data.
• Identifying the important variables and their relations.

People are not very good at looking at a column of numbers or a whole spread-sheet and then determining important characteristics of the data. Exploratory data analysis techniques have been devised as an aid in this situation.

Exploratory data analysis involves use of non-graphical or graphical Methods. Non-graphical methods generally involve calculation of summary statistics(Statistics concepts mentioned above), while graphical methods obviously summarize the data in a diagrammatic way(Visualization concepts mentioned above).

7. Machine Learning for Data Science

Machine Learning(ML) is a branch in artificial intelligence where a set of Algorithms, gives a Computers the ability to learn from data on their own without any human intervention. Computers learn, grow, adapt, and develop by themselves when they are fed with relevant data, without relying on explicit programming. Without data, there is very little that Machines can learn. Example of a ML System is YouTube and Netflix Video Recommendation.

In Data Science, Machine Learning is used for the analysis of data and extraction of useful information from it. Machine Learning basically automates the process of Data Analysis and makes data-informed predictions in real-time without any human intervention.
Learning how to build ML models in data science is important. You should know and implement the following ML Concepts:

• Pipeline
• Linear Regression, Perceptron, and Neural Networks
• Logistic Regression
• Decision Tree
• Naive Bayes
• Support Vector Machines(SVM)
• Ensemble Learning
• Random Forest
• Boosting Algorithms
• Advanced Ensemble Learning
• Hyper-parameter Tuning
• Unsupervised Machine Learning
• K - Means Clustering
• Hierarchical Clustering

8. Model Deployment

For your data science and machine learning project to be available to other users, you need to deploy it. Their are a couple of technologies required to accomplish this:

• Streamlit - This is an open-source python framework for building web apps for Machine Learning and Data Science. You can instantly develop web apps and deploy them using streamlit.
• Amazon Web Services (AWS) - This cloud computing platform that where you can deploy your data science model. AWS services can offer tools such as compute power, database storage and content delivery services.
• Flask - This is a web application framework written in Python. It has multiple modules that make it easier for a web developer to write applications.
• FastAPI - FastAPI is a modern, fast, high-performance, web framework for building APIs with Python. If you want share your data science model as a REST interface, this is the right tool for you.

END NOTE

Data science is the process of asking interesting questions, and then answering those questions using data. Being comfortable with above skills will help you to solve data science problems.

To begin your career in data science, at least be comfortable with python programming language, be able perform data analysis, manipulation, and visualization with pandas and learn machine learning fundamentals.