How To Learn Data Science?
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Data science involves a lot of technical knowledge and processes, but it is also a creative field. You need to have critical thinking and business acumen to define business problems and find relevant data to solve them. You need analytical skills and some creative thinking to find innovative ways to convey your insights to stakeholders and how the insights can help in their business decisions. Terms like business intelligence, machine learning, predictive modeling, data analytics, data mining, visualization are part of data science and form different phases of the data science lifecycle.
Why should you learn Data Science?
Data is the king of business now. A huge amount of data is generated by companies and customers every second and using this data, companies can gain a lot of information about the customers. This helps them make better business decisions and achieve a better hold in the industry. There are applications of data science in every field, be it finance for fraud detection, banking for enabling more secure transactions, healthcare, retail, logistics, supply chain management, and many more. Learning data science gives you a wide range of career opportunities for life. You can explore various domains and skills and specialize in multiple areas as well.
Prerequisites to learn Data Science
As it is, most of the courses and tutorials teach from scratch: Computer science fundamentals, data structures, and algorithms, statistics, math, languages like R/Python, SQL, etc., having knowledge of,
- Basic math concepts like differentiation, integration, linear algebra, etc., will undoubtedly help.
- Same way, knowledge of basic statistical measures like median, mean, etc., and probability will become essential as you take up more advanced courses.
- Knowing at least one programming language, concepts of OOP programming, data structures will be helpful.
Integrated Development Environment (IDE)
How to learn data science majorly depends on how you practice, and IDE’s are the best way to learn. They provide the most features so that you can focus on the important stuff. It is easier to work with IDE because importing libraries, setting up the environment, and code compilation and execution becomes easy. Some of the top IDEs are:
- Jupyter (Python): Although there are many good IDE’s for Python, Jupyter is our personal favorite. We recommend it to all because it is easy to set up and use. It is light as it is based on a web application based on the client-server architecture. You can instantly get started and play around with the code to create visualizations and presentations. You can also convert the final work into PDF or HTML.
- RStudio: For those who prefer using R for data science, RStudio gives a rich experience. It has an open-source version as well as a commercial edition. RStudio provides rich graphics and code completions features along with syntax highlighting and smart indentation. RStudio also provides exhaustive documentation and help functions for developers.
- Scala IDE for Eclipse: Eclipse is a popular IDE for Java and has a similar version for Scala too. You can develop pure Scala and Scala-Java mixed applications and add references from Java to Scala and vice versa. It is fast and catches compilation issues as you write. Eclipse also has a smart indenter that formats the code, provides highlighting support, includes comments, code folding, and so on. With so many features, it is a go-to IDE for Scala/Java-based data science projects.
One of the most popular online platforms is Google Colab, which is built on top of Jupyter and runs on Google Cloud Platform. It supports Python 2 and 3. You can learn to code both machine learning and deep learning algorithms and work with advanced libraries like Keras, OpenCV, TensorFlow, etc. It is free and can be accessed via a browser.
How to learn Data Science
Data Science needs different types of skills as it covers multiple learning areas. If you want to specialize in a particular area or job, for example, you want to be a data engineer, you should focus on those specific skills (like SQL), however for a data scientist, knowledge of all the stages is essential.. For example, you may not get to code algorithms, but you should know the logic behind it, or you may not be involved in plotting the graphs and charts, but you should know how to infer and analyze the visualizations to get the most out of the data.
You should know the following phases of data science:
- Data discovery and collection/acquisition
- Data cleaning and transformation
- Exploratory Data Analysis
- Machine learning techniques
- Evaluate and improve results
Data scientists are responsible for designing data modeling processes to create predictive models based on trends and patterns to find solutions to business problems.
Top 4 Data Science tutorials
No one course is complete in itself for in-depth knowledge of Data Science. We have written a comprehensive article on Data Science courses. Here are the top 5 Data Science tutorials for beginners to get started, and we repeat, get started, with their data science journey:
A-Z is a paid beginner course from Udemy and covers data modeling, mining, visualization using real-world examples. The course covers statistical and machine learning techniques like linear regressions, logistic regression, Chi-square test, confusion matrix, etc. You will also learn how to use Tableau for visualization and SSIS for database interaction. Very practical and hands-on in its approach.
A 10-course specialization that takes about 11 months with about 7 hours per week (you can set your own timelines!) is for beginners to launch their data science career. The specialization has a rating of 4.5/5 from over 80000 reviews. You will learn R programming, data collection, cleaning, EDA, statistical inference, regression models, machine learning, and creating data products that can automate complex tasks.
Although named as ‘intro’, this is an intermediate course, but free of cost. You will be learning how to perform data manipulation, analysis using statistics and machine learning, data visualization, and extensively working with big data (MapReduce). The course requires you to have basic knowledge of any programming language, preferably Python, and a few statistics.
Through this ‘pro’ (paid) course of 35 weeks, you can learn data science from scratch. Topics like SQL, python libraries are also covered in detail. The course is Python-based and uses libraries like numpy, pandas, matplotlib, and scikit-learn extensively. The course is step by step and also includes a portfolio project at the end. It also covers some advanced topics like Natural Language Processing, deep learning, and other SQL and Data Science topics to help you prepare for interviews.
Free Data Science tutorials
Apart from the above tutorials and courses, here are some free resources you can access to learn data science:
Python and R are both top languages for data science. You can learn more about their packages and APIs from the following official documentation pages:
Projects are a great way to start practicing in any field. You can start with simple projects based on linear regression, k-means clustering, decision tree, or Apriori algorithm, which are relatively simple. For example, you can perform market basket analysis, or take up the customer segmentation project, or collect information to determine whether a person is likely to take an insurance policy or not, etc. There are many projects of varying levels in our data science projects article.
Data Science certification will give you an edge over other data scientists with similar experience and help you get more exposure through challenging real-world projects. Certifications are an essential step to learn data science as they boost your resume and showcase your proficiency in the area. We have listed out the Top Data science certifications that can help you land your dream job.
Data science is vast, and you can expect questions from each phase of the data science lifecycle. You should know about machine learning and its types, a little bit about algorithms (or more if you are experienced), deep learning, tools, and techniques for data science like TensorFlow, Tableau, SQL, Python/R/Java, or any programming language that you have used for Data science, and so on. Some typical questions asked in most interviews are:
- Can you enumerate the various differences between Supervised and Unsupervised Learning?
- Could you draw a comparison between overfitting and underfitting?
- Please explain the role of data cleaning in data analysis.
- Please explain Eigenvectors and Eigenvalues.
- What are outlier values and how do you treat them?
- What do you understand by Deep Learning?
- What are the skills required as a Data Scientist that could help in using Python for data analysis purposes?
- What is an Activation function?
- What are the different steps in LSTM?
- What are hyperparameters?
Here is the complete article on Data Science Interview Questions.
To summarize, here is how to learn data science:
- Data science is vast, so you have to decide which job title you want to specialize in.
- Data science includes many sub-fields like data analysis, data engineering, data visualization, machine learning, etc. A data scientist should have expertise in all of these phases of the data science lifecycle.
- Usually, all the introductory courses start with statistics, Python/R, SQL, and other programming concepts in brief, before starting with data science topics. However, it would be advantageous to have a fundamental knowledge of these for a faster learning process.
- Data Science involves various stages: Problem definition and data discovery, data collection, data cleaning and transformation, data visualization and EDA, machine learning, model evaluation, parameter tuning, report generation, presenting the insights to stakeholders.
- There are many free and paid tutorials and courses at various levels for data science. You can do any or all of them based on your learning goals and project requirements.
- Most of the courses have hands-on projects; however, doing more projects will give you more exposure and better domain knowledge.
- You can set goals for yourself and take up certification courses to test and challenge your own capabilities. Certifications will also help you get more practical experience and understand the right expectations of the data science industry.
- The last step, preparing for an interview, is very critical. Here is where you get to know the real industry requirements and the types of questions commonly asked by industry experts. You can practice through the Q & A given on our website and also read other articles on data science for enhancing your knowledge.
Roles & Responsibilities
Some of the key roles of a data scientist are:
- Work with stakeholders to understand the problem(s) faced by the business
- Give a proper definition and structure to the problem and collect data according to that
- Assess the accuracy and relevancy of data, clean and transform the data into a usable form
- Create visualizations to perform an initial analysis of the data and find patterns
- Build algorithms and data models and evaluate the accuracy of models
- Use the insights from the model to make business decisions and improve overall customer experience, thus increasing revenue and market share
- Monitor and evaluate results and work on the feedback based on the results
Some common job titles in a data science career are:
- Data scientists: Design data modeling and create algorithms and predictive models by performing detailed data analysis; communicate the same to stakeholders; develop new user stories from solutions
- Data analysts: Use tools and techniques to transform and manipulate huge data sets and find trends and patterns, and generate useful insights and conclusions leading to better business decisions
- Data engineers: Get raw data from multiple sources, clean, sort, and process data and turn it into usable data for further analysis
- Data architects: Plan, design, create, manage the data architecture of an organization
Many people get confused between the role of data analyst and data scientist. Still, in reality, data scientists have more responsibilities as they are involved in the end to end data science process. Learn more differences between data analysts and data scientists.
The digital world is picking up at a fast pace now, and data science will remain a crucial part of business expansion for years to go. With the popularity of AI, Natural Language Processing, and other related technologies, data science will find more demand in more domains than it already has, thus generating more job opportunities in data science and related fields. Programmers, DB admins, big data engineers, software architects, and data analysts will be high on-demand, along with data scientists who will have a significant role to play in managing all the above resources as well as perform their own tasks. Glassdoor has listed the data scientist role in their top 10 best jobs in the USA. LinkedIn also recently rated data science as the top emerging job of 2021.
If all the above doesn’t motivate you enough, let us inform you that a data scientist role is one of the most highly paid jobs in the IT industry now! The average salary of a data scientist in the US is about $96,315 per year, and the highest is about $136,500 (for managers). Not only that, most data scientists, as per PayScale’s rating, are highly satisfied with their jobs (4/5). A data science director is typically paid $157095 per year. Mainly, machine learning, big data analytics, Python programming, Algorithm development, NLP are cream areas of data science.
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