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Learning Path of Data Science - [2022 Update]

pkt123

@pkt123

September 30, 2022

Data Science & AI Community

299

Introduction

Data Science is a multidisciplinary approach that combines technology and data analysis to address a wide range of challenging analytical issues. A data scientist's job is to use the data in a variety of innovative ways to benefit the company. Data science is a self-learning path about creating discoveries, posing new questions, and picking up new knowledge.

Data scientists can use their imagination and inventiveness to find solutions to challenging issues. They have a strong passion for taking on problems and are perpetually curious.

The conventional talents of data mining, evaluating vast volumes of data, and programming are being advanced by effective data professionals today. Studying a data science learning path is the best method to become a data scientist. It is highly beneficial for the candidate to achieve his goals as a data professional by understanding the data science learning path.

The following tasks fall within the data science learning path:

Understanding what a data scientist does
Knowing the fundamentals of statistics
Studying machine learning principles
Gaining an understanding of deep learning
Natural Language Processing
Building a GitHub profile
Reinforcement learning: A recent development in data science

Understanding a data scientist's job

Understanding the function of a data scientist is the first step on the learning curve for data science. In essence, a data scientist's job is to gather and analyze data using various methods. Then they present that data in a visual format, a process known as "visualizing the data." They can develop sophisticated algorithms for identifying patterns. Data scientists have been taught how to collect, organize, and analyze data. Anyone can code following the criteria by being aware of all the roles a data scientist plays.

Knowing the fundamentals of statistics:

The fundamental ideas that a data scientist has to master are mathematics and statistics. A mathematical science that deals with analysis, data collection, and interpretation is known as statistics. If a user is unfamiliar with the fundamentals of statistics, learning about a new tool will take a lot of time. In this case, swift computations are required to produce the findings very quickly. Data scientists should understand the descriptive, probability and inferential statistical approaches. They should be proficient in the mathematical area of linear algebra.

Understanding machine learning concepts:

Machine learning technologies are employed to gain a competitive edge by utilizing data. One who is really interested in data science needs to be familiar with machine learning concepts and should learn how to use ML techniques. Examples of machine learning ideas include boosting algorithms, ensemble learning, random forests, and time series approaches. The data scientists should concentrate on business applications while also being familiar with clever machine learning techniques.

The fundamental machine learning algorithms are as follows:

Rational Regression
Nearest Neighbors in K
Simple Bayes
Regular Regression
Choice Trees
K-Means
Diminished Dimension
Stability Vector Machines
Rough Forests
XGBOOST
Machines that boost the gradient

Gaining an understanding of deep learning

One who is interested in pursuing a career in data science should focus on comprehending deep learning after being familiar with machine learning ideas. It is quite simple to employ many layers to extract high-level characteristics from the raw input once you have a basic understanding of deep learning. In addition to deep learning, we also need to understand computer vision applications. A type of artificial intelligence called computer vision is used to teach computers how to comprehend and interpret the visual world. The machines can classify and identify items using digital photos using deep learning models.

Natural Language Processing

The learning path for data science cannot be completed without understanding Natural Language Processing (NLP). Computer science, linguistics, and artificial intelligence all have a subfield called NLP. Speech recognition, natural language production, and natural language understanding are among the difficulties in NLP.

Python programming is the core skill of a data scientist. The candidate should be able to run Python code in a variety of ways and should feel at ease using it. If a candidate is familiar with data analysis and machine learning, he or she must also be able to manipulate and visualize data. Having expertise with projects like Pandas, Numpy, and Matplotlib benefits the candidate profile.

Building a GitHub profile:

A data scientist must have a GitHub profile because it houses all of the candidate's completed project codes. As a professional worker, how you code is more important than what you are coding. The candidate's learning can be further by using the open source projects on the GitHub codes.

Reinforcement Learning: A New Data Science Trend

One of the machine learning techniques that aid in the intuitive learning of data professionals is reinforcement learning. Comparing reinforcement learning to unsupervised learning reveals certain differences. It is a significant development in data science and has significant promise for use in AI and proactive analysis. RL also involves complex algorithms and uses less sophisticated tools. Google began applying DeepMind's Reinforcement Learning in 2016 to determine the available power in data centers. Later, Microsoft makes use of the RL subset known as contextual bandits. These contextual bandits were changed into Microsoft's Multi world Testing Decision Service within a few months. Those who desire to work as data scientists can gain an edge by understanding the principles of reinforcement learning.

Conclusion:

For people who want to learn data science and machine learning, the Learning route of data science is quite helpful. This post is helpful for those persons in creating a solid action plan for their preparation without any misunderstanding because the main issue for data science seekers is simply the abundance of learning material and irrelevant planning in studying.

Data Science Data science course in Bangalore data science course data science training data science certification course in bangalore

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