Getting Started with Data Science
Starting with data science is not that thought, you just need to start from basic and then go in-depth with other advance areas of Data Science. The step-by-step process of learning about data science mainly consists of five main steps:
1. Learning Programming Language
Learning one programming language is crucial for starting with the data science journey, as a programming language helps to sort the data, analyze and help to manage large chunks of data. Python is a good start as it will provide numerous modules and libraries that help to get output more easily as compared to other languages. You need to learn the basics of Python such as datatypes, defining variables, etc. The two most important libraries that you need to understand are NumPy and Pandas. To work with data these are important to learn. Apart from Python other popular programming languages are
2. Learn Statistics
Statistics are the foundation of Data science on which data analysis, machine learning, and predictive modeling work. Most of the ML models have underlying statistical assumptions. Learning basic data such as statistics helps to make informed decisions for businesses and with the help of hypothesis testing. you can test new features and analyze hypotheses of applications by using statistics. Predictive models use statistical algorithms to verify patterns and predict data for the future. You need to revise the previous topics that are
- Descriptive Statistics
- Probability
- Probability Distribution
- Inferential Statistics
- Regression Analysis
- Advance Topics include chi-squared tests, ANOVA, and more
3. Data Visualization
Visualizing the data in the form of Charts, tables, and graphs is important for data scientists. Making well-designed charts or graph which summarizes a thousand data points helps to visualize and interpret the given data easily. In Data visualization, there are two important libraries in Python to learn which are Matplotlib and Seaborn. Having proper knowledge about the tools is also important that will help you to prepare work in a proper manner.
4. Machine Learning
Machine Learning and Artificial Intelligence is one of the most important parts of Data Science. Machine learning is an integral part of Data Science that includes Predictive analysis of data, It helps to analyze a large chunk of data automatically. Machine learning automatically helps to process data analysis and makes data for real-time prediction with no human interaction in between. This is where it works in Data Science. Firstly It gets data from the datasets, then after it Clean and manipulates data as needed, After Testing the data if the data is not perfect or not ready for deployment then the model is further improved according to the given parameters. From showing favorable movies on streaming platforms to suggesting products that are required on an e-commerce website this all happens with the prediction and analysis of machine learning. The techniques that you need to learn in machine learning for data science are
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Deep Learning
- Ensembles Methods like Gradient Boosting or Random Forest.
For more detailed Roadmap to Data Science you can refer to – How to Become Data Scientist – A Complete Roadmap
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A Day in the Life of a Data Scientist
What comes to your mind when you hear the word Data Science? Data Science is not just about writing code, algorithms, and formulas. But Data Science is all about collecting raw data, analyzing that data, and providing us with insights that can be used to make decisions. Who is the mastermind behind it? The Data Scientist is a role that is highly demanding in the tech industry and holds some of the most competitive salaries in the industry. They help to find patterns in big datasets and they have expertise not only in data science but also in Big data, Python, R programming, and SAS.
Table of Content
- What Does a Data Scientist Do?
- A Typical Day in the Life of a Data Scientist
- Getting Started with Data Science
- 1. Learning Programming Language
- 2. Learn Statistics
- 3. Data Visualization
- 4. Machine Learning
- Future Scope of Data Science
- Conclusion
Nowadays, as technology is developing, the number of users is increasing and the data is now termed as Big Data. Data Scientists are important in solving the mysteries of Big data and patterns. Suppose it’s like having a giant jigsaw puzzle and when you solve it, you can figure out trends, make your business better, and even make your life better. That’s what data scientists do every day with tools and algorithms and their sharp minds. It helps our businesses to grow and make proper decisions.
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