Data science is the study of data, much like marine biology is the study of sea-dwelling biological life forms. Data scientists construct questions around specific data sets and then use data analytics and advanced analytics to find patterns, create predictive models, and develop insights that guide decision-making within business logics.
Roadmaps are strategic plans that determine a goal or the desired outcome and feature the significant steps or milestones required to reach it.
A data science roadmap is a visual representation of a strategic plan designed to help aspiring those who aspire aspiring to learn and succeed in the field of data science.
Key Tools used in Data Science
we’ll take a look at key data science tools that will help on your data science roadmap journey successful.
- Programming Languages - Here we have different programming languages that you need to master and know how to use them. Examples are;
R-Language - is similar to python and a famous programming language for working with data. it's a powerful language for performing data wrangling with dplyr and ggplot2 to create any kind of chart you might need.
Python - Python is one of the greatest options available to you. In python, you can take advantage of the following libraries under a package known as Jupyter Notebook;
a) Pandas
b) Matplotlib
c) Scikit-learn
SQL - Stands for Structured Query Language). SQL allows the user to insert, update, delete, and select data from databases and to create new tables. The most common way to interact with these databases — called relational databases--is through Structured Query Language, or simply SQL.
2.Machine Learning Libraries
In ML there are libraries you need to get familiar with like TensorFlow, Scikit learn, Pandas, Matplotlib, Numpy and NLTK.
3.Data Visualization Tools
Data visualization tools are software applications that render information in a visual format such as a graph, chart, or heat map for data analysis purposes. Such tools make it easier to understand and work with massive amounts of data. Examples are PowerBI, Tableau and Matplotlib.
4.Data Storage Software
Learn about data storage software like SQL, MySQL, PostgreSQL and MongoDB
5.Cloud Computing Platforms
These includes AWS - Amazon Cloud Services, Microsoft Azure and Google cloud services. by learning these, you will be able to interact with cloud storage services with your local stored data.
Learn about programming software Engineering
When you begin your data science roadmap, you must have a solid foundation. The data science field requires skills and experience in either software engineering or programming. You should learn a minimum of one programming language, such as Python, SQL, Scala, Java, or _ R._
Example Programming Topics to learn
Data scientists should learn about common data structures (e.g., dictionaries, data types, lists, sets, tuples), searching and sorting algorithms, logic, control flow, writing functions, object-oriented programming, and how to work with external libraries.
Additionally, aspiring data scientists should be familiar with using Git and GitHub-related elements such as terminals and version control.
Finally, data scientists should enjoy a familiarity with SQL scripting.
Learn Git and Github
Git and Github allows you as a data scientist to push your ready-made projects. This will make you share with the outside world and learn more about git concepts and rules of writing Git files and also sharing with others.
Top comments (0)