The global ML and data science market is expected to grow rapidly and reach around $20.83 billion within 2024, which is huge. Top companies like Microsoft, Zomato, Google, Swiggy, and Byju rely on Machine learning and Data science technology to streamline their business operation. This makes data science the fastest-growing and demanding field in the job market. The average salary of a data science professional is around Rs 900k every year and Rs400k- of a computer programmer in India, according to Glassdoor. Data science application and benefits are endless as everything starting from self-driven cars to sales prediction is powered by data science technologies.
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Roadmap for a data science career.
There is a shortage/ scarcity of skilled data scientists even though job opportunities are rapidly increasing.
To be successful, data scientists need to accomplish data science training and possess important skills that make them industry-ready experts.
Fundamentals of data science
The foremost skill to learn to be a data scientist is understanding data science fundamentals before applying SVM, a Linear regression type machine learning technique. Your basics relate to artificial intelligence, machine learning, data science must be clear. Cover the following topics-
- Data science tools
- Regression vs classification problem
- Difference between deep learning and machine learning.
- Unsupervised vs supervised learning etc.
Statistics and probability
All machine learning models and concepts are based on statistical analysis. Statistics is considered data science’s grammar which is important to build high-quality models. You need to dive deep into descriptive statistics concepts like standard deviation, mean, mode, median etc., to become a data scientist and learn probability distribution like hypothesis testing, CLT, inferential statistics, skewness, and kurtosis.
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Programming knowledge
The next important skill is programming knowledge that helps in communicating with machines. Learn a suitable programming language of your preference. For instance, some of them are- Python, R etc. A general programming language with rapid prototyping that is easy to learn and has plenty of data science libraries is known to be Python. On the other hand, R is a visualization and statistical analysis language. These programming languages support deep learning.
Data manipulation and analysis
Data manipulation or data wrangling helps to make data-driven decisions by cleaning data and converting it into a better format that will help in proper analysis in further stages. It is applied in outlier treatment, scaling, missing value imputing, correction of data types etc. Data analysis skill helps you to feel data and learn a lot about it. It is the main step in machine learning data analysis and is done in Python, excel, pandas, and SQL.
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Data visualization
Data science requires you to be an expert in data visualization. It helps to know plots such as pie charts, histograms, and bar charts and then move to thermometer charts, waterfall charts, and other advanced charts. An expert in data visualization knows building stories out of visualization.
Machine learning
One core skill to learn to become a successful data scientist is to learn machine learning. Predictive models are built using machine learning. A data scientist uses machine learning algorithms to look at previous data and predict future outcomes. Problem statements need to be practiced along with logistic regression, linear regression, etc.
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Read more blogs about :
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- How data science helps local government
- Why Data Science Is Important And Why Do We Want It?
- How To Become An Data Scientist With No Experience
- Why Is Data Science An Interesting Career?
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