Learning Path of Data Science in 2020
Introduction: Data Science is a multidisciplinary blend of technology and data interference to solve many complex analytical problems. The purpose of a Data Scientist is to use the data in so many cr...
Data Science is a multidisciplinary blend of technology and data interference to solve many complex analytical problems. The purpose of a Data Scientist is to use the data in so many creative ways to generate value to the business. Data Science is a self-learning path, all about making discoveries, asking new questions, and learning new things. Data Scientists can solve hard problems by using their ingenuity and creativity. They are passionate about taking challenges and indulge themselves constantly in curiosity.
Nowadays, successful data professionals advancing the traditional skills of data mining, analyzing large amounts of data and programming skills. The best way to become a data scientist is to learn data science learning path. To get in-depth knowledge of the data science learning path, one must do data science certification, so that it is very helpful for the candidate to reach his goals as a data professional.
The following steps come under the learning path of data science:
Knowing the role of a Data Scientist:
The biggest step in the learning path of data science is knowing the role of a data scientist. Essentially, the role of data scientists is to analyze and gather the data by using different techniques and they present the data in a visual contest which is also called as “visualizing the data”. They can create advanced algorithms that are used for the determination of the patterns. Data Scientists are trained to organize, gather, and analyze data. By knowing all the roles of a data scientist, anyone can able to code according to the requirements.
Understanding the basics of Statistics:
Mathematics and Statistics are the core concepts that a data scientist must learn. Statistics is referred to as a mathematical science pertaining to analysis, data collection, and interpretation. While learning about a new tool, takes a lot of time to understand if the user doesn’t know the basics of statistics. Here, quick calculations are needed to generate the results very fast. The data scientists should grasp the descriptive, probability, and inferential statistical methods. They should also have knowledge in linear algebra mathematical field.
Learning the concepts of Machine Learning:
Machine learning technologies are used to achieve competitive advantages by putting data to work. One who is very much interested in data science, he must know the concepts of machine learning and should learn the applications of ML algorithms. The machine learning concepts are boosting algorithms, ensemble learning, random forest, and time series methods. The data scientists must know nifty tricks of machine learning and they also should focus on industry applications.
The following are the basic machine learning algorithms:
Acquiring the knowledge of Deep Learning:
After knowing the machine learning concepts, one who is enthusiastic in the data science learning path must concentrate on understanding deep learning. By acquiring the knowledge of deep learning, it is very easy to use multiple layers to extract high-level features from the raw input. Other than deep learning, here we have one more subject to learn that is computer vision applications. Computer vision is a subset of Artificial Intelligence that is used to train the computers to understand and interpret the visual world. By using digital images from deep learning models, the machines can classify and identify objects.
Including Natural Language Processing:
Without learning the Natural Language Processing (NLP) there is no completion of the data science learning path. NLP is the subfield of computer science, linguistics, and artificial intelligence. The challenges of NLP include natural language understanding, speech recognition, and natural language generation.
The fundamental skill of a data scientist is python programming. The candidate should feel comfortable with Python syntax and can run in many different ways. If the candidate already has the knowledge of data analysis and machine learning then he must know how to visualize and manipulate data. By mastering in projects like Pandas, Numpy and Matplotlib add an advantage to the candidate profile.
Building Profile on GitHub:
For a data scientist, it is important to have the GitHub profile, it is because to host all the project codes that a candidate has undertaken. It is not what you are coding, it is how you are coding is matters as a professional employee. The GitHub codes are avenues for open source projects that can boost the candidate’s learning.
Reinforcement Learning (New Trend in Data Science):
Reinforcement learning is one of the methods in machine learning that helps data specialists to learn in an intuitive atmosphere. Reinforcement learning is distinctive when compared to unsupervised learning. It is a huge thing in data science and it’s potential worth in proactive analysis and Artificial Intelligence is huge. In addition, RL uses less advanced tools and involves complicated algorithms. In the year 2016, Google started utilizing Reinforcement Learning of DeepMind to understand the spare power in data centers. Later on, Microsoft utilizes the subset of RL called contextual bandits. Within a couple of months, Microsoft transformed these contextual bandits into the Multi world Testing Decision Service. By knowing the concepts of this Reinforcement Learning can make an extra advantage for those who want to become a data scientist.
The Learning path of data science is extremely useful for those who want to learn data science and machine learning. For the people who are looking for an action plan of data science learning path, this article helps them very much as a guide. The biggest challenge for the seeker of data science comes only because of the too much learning material and irrelevant planing in learning, this article is useful for such kind of people in setting up a proper action plan for their preparation without any confusion.
Source: Free Articles from ArticlesFactory.com
ABOUT THE AUTHOR
Anji Velagana, a graduate in Electronics and Communication Engineering from Jawaharlal Technological University, Kakinada. He is currently working as a Digital Marketing Analyst and Content Contributor for Mindmajix. He writes about various platforms like Data Science, Servicenow, Business analysis, Performance testing, Mulesoft, Oracle Exadata, SaltStack, and few other courses. Contact him via firstname.lastname@example.org and LinkedIn.