How to get job in data science
The process of data visualization involves taking raw data and presenting it in a graphical or pictorial format. This allows people to easily understand and interpret the data, making it useful for decision-making and analysis. Data visualization tools, such as charts and graphs, can be used to display data in various formats, such as bar charts, line graphs, and pie charts. These tools can be used in a variety of fields, including business, finance, and science, to help people make sense of large amounts of data
Top ten job for data science
Data Scientist
Machine Learning Engineer
Data Engineer
Business Intelligence Analyst
Big Data Engineer
Data Analyst
Data Architect
Research Scientist
Data Consultant
Statistical Analyst
Note that this is a general list and may vary depending on the specific industry and company. Additionally, the job titles and duties may also change over time as the field of data science continues to evolve.

What is salary of data science
The salary for data science positions can vary widely depending on a number of factors such as location, industry, level of experience, and type of employer. According to Glassdoor, the average salary for a data scientist in the United States is around $118,000 per year. However, salaries can range from around $70,000 to $170,000 or more. Other sources suggest that salaries can be higher for data science roles in certain industries, such as technology and finance, and for those with specialized skills and experience. Additionally, location can also play a role in determining salary, with data science roles in major cities like San Francisco and New York typically paying more than those in other parts of the country.

Data science salary in India
The salary for data science positions in India can vary widely depending on a number of factors such as location, industry, level of experience, and type of employer. According to Glassdoor, the average salary for a data scientist in India is around 8-15 LPA (Lakhs per annum) per year. However, salaries can range from around 5 LPA to 20 LPA or more. Other sources suggest that salaries can be higher for data science roles in certain industries, such as technology and finance, and for those with specialized skills and experience. Additionally, location can also play a role in determining salary, with data science roles in major cities like Bengaluru, Mumbai and New Delhi typically paying more than those in other parts of the country.

Top course for data science
Data Science Specialization on Coursera by Johns Hopkins University
Machine Learning by Andrew Ng on Coursera
Data Science A-Z: Real-Life Data Science Exercises Included on Udemy
Introduction to Data Science in Python by University of Michigan on Coursera
Applied Data Science with Python Specialization by University of Michigan on Coursera
Data Science Essentials by Microsoft on edX
Data Science Methodology by IBM on Coursera
Data Science and Machine Learning Bootcamp with R on Udemy
Data Science and Machine Learning Bootcamp with Python on Udemy
Data Science and Machine Learning with Python by IBM on Coursera
Note that this is a general list and may vary depending on the specific area of focus, level of experience, and learning style of the individual. These are popular courses and have good reviews but there are many other options out there and you should evaluate the course based on your need and requirement.

What is criteria for become data scientist
To become a data scientist, one typically needs to have a strong background in mathematics, statistics, and computer science, as well as expertise in machine learning and programming. The specific requirements can vary depending on the employer and the specific role, but some common qualifications include:
A degree in a relevant field such as computer science, mathematics, statistics, or electrical engineering.
Strong knowledge of programming languages such as Python, R, SQL, and Java, and experience with data science libraries and frameworks such as TensorFlow, scikit-learn, and Pandas.
Strong understanding of statistical techniques and machine learning algorithms, including supervised and unsupervised learning, and experience with data visualization tools such as Tableau, Matplotlib, and ggplot.
Strong problem-solving skills and the ability to work with large and complex data sets.
Experience with big data technologies such as Hadoop and Spark, as well as cloud-based data storage and processing solutions such as AWS and Azure.
Strong communication and teamwork skills, as data scientists often work with cross-functional teams and need to be able to explain complex technical concepts to non-technical stakeholders.
Continual learning, as the field of data science is rapidly evolving and new technologies are constantly emerging.
Having a portfolio of projects to demonstrate your skills and experience.
This is a general list and the qualifications required may vary depending on the specific employer and role. However, having a combination of education, experience and skills in the field of data science will make you a strong candidate for data science roles.

Data scientist job market
The demand for data scientists has been growing rapidly in recent years, with many companies and organizations recognizing the value of data-driven decision-making. According to the Bureau of Labor Statistics, the employment of statisticians and data scientists is projected to grow 16% from 2019 to 2029, much faster than the average for all occupations. This growth is driven by the increasing use of data and analytics in a wide range of industries, including finance, healthcare, technology, and retail.
As companies and organizations continue to generate and collect large amounts of data, they need professionals who can analyze and interpret that data to make informed business decisions. Data scientists are in high demand because they have the skills and knowledge to turn raw data into actionable insights.
There are many different job titles that fall under the data scientist umbrella, such as data analyst, data engineer, and machine learning engineer, and they can work in a variety of industries and settings, including tech companies, consultancies, healthcare, finance, retail, and more.
Data science jobs are available all over the world, with the highest demand for data scientists in the United States, United Kingdom, Canada, and India. However, the competition for these roles can be intense and employers often look for candidates with strong educational backgrounds, relevant work experience, and a portfolio of data science projects.
Overall, the data scientist job market is very active, with high demand and a wide range of opportunities available. As the field of data science continues to grow, so too will the job market for data scientists, with many opportunities for those with the right skills and experience.

Who can be data scientist
A data scientist is a professional who uses statistical, programming and data analysis skills to extract insights and knowledge from data. Anyone with the right skills, education and experience can become a data scientist.
Typically, data scientists have a background in mathematics, statistics, and computer science, as well as expertise in machine learning and programming. They may have a degree in a relevant field such as computer science, mathematics, statistics, or electrical engineering, or a related field such as physics, economics or operations research.
Data scientists need to have strong problem-solving skills and the ability to work with large and complex data sets. They should be proficient in programming languages such as Python, R, SQL, and Java, and have experience with data science libraries and frameworks such as TensorFlow, scikit-learn, and Pandas. They should also have a strong understanding of statistical techniques and machine learning algorithms, including supervised and unsupervised learning, and experience with data visualization tools such as Tableau, Matplotlib, and ggplot.
Data scientists also need to have strong communication and teamwork skills, as they often work with cross-functional teams and need to be able to explain complex technical concepts to non-technical stakeholders.
It’s not only the degrees and academic qualifications that are important but also the experience and skills that someone has gained in the field of data science. A person with a combination of education, experience and skills in the field of data science can become a data scientist.
So, anyone who has the right skills, education and experience and is willing to continuously learn and adapt to the rapidly evolving field of data science can become a data scientist.
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