Introduction

What is data Science ?

Data science is an interdisciplinary field that use scientific techniques, procedures, algorithms, and systems to extract information and insights from noisy, structured, and unstructured data, and then applies that knowledge and actionable insights across a wide variety of application areas.
Roadmaps are strategic plans that determine a goal or the desired outcome and feature the significant steps or milestones required to reach it.
Therefore this is a roadmap to becoming a great data scientist.

The Data Science Lifecycle
Data science’s lifecycle consists of five distinct stages, each with its own tasks:

1.Capture: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.

2.Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture. This stage covers taking the raw data and putting it in a form that can be used.

3.Process: Data Mining, Clustering/Classification, Data Modeling, Data Summarization. Data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis.

4.Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the lifecycle. This stage involves performing the various analyses on the data.

5.Data Reporting, Data Visualization, Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.

1.Programming

First and foremost , and this is highly ignored , to become a data scientist you will requires skill 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.

Data science sits at the intersection of analytics and engineering, therefore a combination of mathematical skills and programming expertise is relevant.
A Data scientist with software skills will be a more desirable candidate.
Programming has been cited as the most important skill for a data scientist. A data scientist with a software background is a more self-sufficient expert who does not need outside resources to work with data in that they’re able and write scripts for querying the data on their own without using a blackbox tool or an engineer. For a variety of reasons, software skills greatly benefit a data scientist.

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.

2. Mathematics

Mathematics (algebra, calculus, optimization, and functions) is the backbone of data science. The most critical step in the data science process is Exploratory Data Analysis (EDA), which entails conducting statistical experiments and performing matrix operations. This step requires extensive knowledge of math, including linear algebra, statistics, mathematical analysis, and more.
When you consider what concept of mathematics needs for studying data science.

Here are the three main elements:

_Linear Algebra _
– Computers use linear algebra for carrying out calculations efficiently.
-Almost all models will require computations through linear algebra.

_Calculus _
-For in-depth knowledge of data science, calculus is something that one must not skip.
-It is essential in the development of mathematical models that help in increasing accuracy and performance.

Statistics & Probability

Both are used in machine learning and data science to analyze and understand data, discover and infer valuable insights and hidden patterns.

Mathematics is very important in the field of data science as concepts within mathematics aid in identifying patterns and assist in creating algorithms. The understanding of various notions of Statistics and Probability Theory are key for the implementation of such algorithms in data science. Notions include: Regression, Maximum Likelihood Estimation, the understanding of distributions (Binomial, Bernoulli, Gaussian (Normal)) and Bayes’ Theorem.

3. Databases

Although this can be accomplished by data engineers rather than data scientists, it is essential that the data scientist be able to query and manipulate it, which means they should learn database principles.

Additionally, database tools often require programming. Using SQL to query a database is a key function of the data scientist’s role. While one can learn SQL without a software background, having the knowledge of programming that comes from developing software skills is useful in writing more efficient SQL queries.

4 .Machine Learning (ML):

Machine learning as a type of artificial intelligence (AI) or a subset of AI which allows any software applications or apps to be more precise and accurate for finding and predicting outcomes.

Machine learning algorithms use historical data to predict new outcomes or output values. There are different use cases for machine learning like fraud detection, malware threat detection, recommendation engines, spam filtering, healthcare, and many others.

As a data scientist , it is important for every data scientist to be familiar with as many ML algorithms as possible, as it is crucial to be able to choose the best model that best fits the problem they are working on. These algorithms include Classification, Regression, and other algorithms.

In some real-life scenarios — online recommendation engines, speech recognition (in Siri and Google Assistant), detecting fraud in all the online transactions — data science and machine learning work together and give valuable data insights. Thus, it will not be wrong to infer that Machine Learning can analyze data and extract valuable insights.

Data Science and Machine Learning complement each other, with machine learning making the life of a Data Scientist easier.
Machine learning can be of different types:

Supervised learning : machines are trained to find solutions to a given problem with assistance from humans who collect and label data and then “feed” it to systems. A machine is told which data characteristics to look at, so it can determine patterns, put objects into corresponding classes, and evaluate whether their prediction is right or wrong.

unsupervised learning : machines learn to recognize patterns and trends in unlabeled training data without being supervised by users.

semi-supervised learning : models are trained with a small volume of labeled data and a much bigger volume of unlabeled data, making use of both supervised and unsupervised learning.

Reinforcement learning : models put in a closed environment unfamiliar to them, must find a solution to a problem by going through serial trials and errors. Similar to a scenario found in many games, machines receive punishment for an error and a reward for a successful trial. In this way, they learn to find an optimal solution.

5. Deep Learning (DL):

Deep learning is a subset of machine learning, but it is advanced with complex neural networks, originally inspired by biological neural networks in human brains. Neural networks contain nodes in different interconnected layers that communicate with each other to make sense of voluminous input data.

Although ML can solve a large portion of data science problems, some require a more complicated model that can deliver sufficient results; therefore, every data scientist should be familiar with deep learning. It is also critical to learn how to work with frameworks. TensorFlow, PyTorch, and JAX are the most popular.
Deep learning can process both unlabeled and unstructured data. This learning method also creates more complex statistical models. With each new piece of data, the model becomes more complex, but it also becomes more accurate

Conclusion

In data science roadmap article, we have seen the key stages of data science and related resources , we have also seen that data science is a very big field, and there are a lot of things to learn.

You can do your own research to learn more about data science. A good data scientist, must become a good researcher.