How to Switch Your Career to Data Science?

Undeniably, Data science has become one of the hottest industries over the past few years from now. Being dominant in almost every sector, data science is powering up businesses (small-mid-large) and helping them in making business decisions and that’s what makes it special and demand is rising like a storm in the market for such professionals. In fact, people with no such background have also taken their way toward data science and by going through different processes many have made a career transition. 

Data Science is the study of data using tools and technologies to build predictive models and derive meaningful information. Career transition helps you in getting a “Handsome Salary” and alongside expanding your knowledge in various sectors. This is something called A Good Call. Now, the question arises, if you’re already working in some domain then “How to switch your career in Data Science?” and to make your way smooth and provide you in-depth details, we have drafted this article that will guide you through all the way so that you can start your new path towards data science.

Why “Data Science” is in Demand?

The growth of data scientists has grown enormously in the past few years and the urge for data scientists and their importance have been acknowledged by almost all major industries and that’s one of the reasons why the demand has grown above 300% in just 8-9 years. As per a survey, the YoY growth has been reported at 30% and it’s going to rise at a slightly high pace in upcoming years. 

Likewise, the demand for data scientists has risen having 36000+ job openings in India and above 5,00,000 (worldwide) and that’s a huge number that has been growing significantly and the demand has risen from all across the industries with a whooping 2500-3000 every month. Now the industries have understood the importance of Data Science on how they impact making their “business” process smoothly.

While companies realize the value and power of Big Data, they thrive to make use of it to make better business decisions. Although there might be a lot of other reasons, still few are the major key highlights that make it “Most Wanted” jobs in the industry.

1. The complexity of Handling Data
2. Lack of Resources
3. Hard to find professionals with the know-how and technical skills
4. Proficient in deciding the business forecast
5. Helps in cost reduction for unwanted expenditures

These are some of the factors that make it difficult for companies to hire such professionals in data science.

How Can You Start as a “Beginner”?

Since data science requires specific skillsets by which the individual should be capable enough to perform certain tasks on different projects as required. Thus, for every domain in data science, you must possess different skill sets to handle every possible complexity. You can have different roles such as Data Analyst, Data Scientist, or Business Analyst, and for qualifying for any of the roles, you must know from scratch, especially if you’re a beginner. Let’s take a look at the step guidance towards the learning path.

First, you’ll need to figure out what you need to learn during your initial phase the moment you’ve decided to begin your career in “Data Science”.

Where to Begin?

• Proficiency in Mathematics: Although there are other factors that are required to start your career whereas mathematics and some parts of it are definitely required to become a data science expert.
• Programming Language: If you’ve read something before deciding to start your career in this stream then you must have understood that having command of a programming language (such as Python, R, etc.) is a must and you should be capable enough to manage those data with programming languages. 
• Domain Knowledge: One must have complete knowledge of what “data science” is and how can it be applied using various frameworks and tools to get the desired output.

Step I

Fundamentals of Mathematics

Linear Algebra & Mathematics

• System of Linear Equation
• Matrix Operation
  • Addition, Multiplication, Division Using Python
  • Addition, Multiplication, Division Using NumPy
  • Inverse
  • Transpose
• Properties of Matrix
• Solving Linear Equation using Gaussian Elimination 
• LU Decomposition of Linear Equation
• Row Echelon Form
• Determinant
• Trace
  • With Numpy
• Eigenvalues and Eigenvectors
• Eigenspace
• Orthogonal and Orthonormal Vectors
• Cholesky Decomposition
  • With NumPy
• Eigen Decomposition
• Diagonalization
• Singular Value Decomposition — Implementation
• Matrix Approximation
• Vector Operations
• Linear Mappings
• Affine Spaces

Statistics

• Mean, Standard Deviation, and Variance — Implementation
• Sample Error and True Error
• Bias Vs Variance and Its Trade-Off
• Hypothesis Testing
  • T-test
  • Paired T-test
  • p-value
  • F-Test
  • z-test
• Confidence Intervals
• Correlation and Covariance
• Correlation Coefficient
• Covariance Matrix
• Pearson Correlation
• Normal Probability Plot
• Q-Q Plot
• Residuals Leverage Plot
• Pearson Product-Moment Correlations
• Spearman’s Rank Correlation Measure
• Kendall Rank Correlation Measure
• Robust Correlations
• Evaluation Metrics – Accuracy, Precision, Recall, F1-Score, MAE, MSE
• RMSE and R-Squared Error
• Precision-Recall Curve
• ROC-AUC curve

Geometry

• Vector Norms
• Inner, Outer, Cross Products
• Lengths and Angles
• Orthogonality and Orthonormal Vectors
• Orthogonal Projections
• Rotations
  • With NumPy
• Piecewise Functions
• Constraints and Splines
• Box-Cox Transformation using Python
• Fourier transformation
• Inverse Fast Fourier Transformation

Calculus

• Function Differentiation
• Implicit Differentiation
• Inverse Trigonometric Functions Differentiation
• Logarithmic Differentiation
• Partial Differentiation
• Advanced Differentiation
• Gradients
• Gradients of Matrices
• Useful Identities for Gradient Computation
• Backpropagation
  • Gradient Descent Algorithm
• Higher-Order Derivatives
• Multivariate Taylor Series

Probability & Distributions

• Probability
• Chance and Probability
• Discrete and Continuous Probabilities
• Addition Rule for Probability
• Law of total probability
• Sum Rule, Product Rule, and Bayes’ Theorem
• Uniform Distribution
• Normal Distribution
• Poisson Distribution
• Exponential Distribution
• Binomial Distribution
• Gaussian Distribution
• Central Limit Theorem
• Conjugacy and the Exponential Family
• Change of Variables/Inverse Transformation

Regression

• Parameter Estimation
• Bayesian Linear Regression
• Normal Equation in Linear Regression
• Maximum Likelihood as Orthogonal Projection

Dimensional Reduction

• Introduction to Dimensionality Reduction
• Projection Perspective
• Eigenvector Computation and Low-Rank Approximations
• Principal Component Analysis (PCA)
• PCA implementation in Python
• Latent Variable Perspective
• Low-Rank Approximations
• Overview of Linear Discriminant Analysis (LDA)
• Mathematical Explanation of Linear Discriminant Analysis (LDA)
• Generalized Discriminant Analysis (GDA)
• TSNE Algorithm

Vector Models

• Separating Hyperplanes
• Primal Support Vector Machine
• Dual Support Vector Machine
• Kernels 

These are the topics that you’ll be required to cover in “Mathematics” to get your roots strong. Right after, you’ll be required to choose the appropriate programming language to get started with data science. Which one to pick and how to pick? Will see about that in the next step.

Step II

Selection of Programming Language 

Programming Languages you can opt for –

• Python
• R
• Scala
• Go
• MATLAB
• Swift
• SQL

Step III

Required Tools & Frameworks

1. For Business Intelligence

• SAP Business Objects
• Datapine
• Microstrategy
• SAS Business Intelligence

2. Statistical Analytical Tools

• Apache Spark
• Microsoft Power BI
• Tableau
• Jupyter Notebook

3. Automation Tools

• DataRobot
• Darwin
• RapidMiner
• Automatic Business Modeler (ABM)

4. Data Modeling Tools

• ER/Studio
• Lucid chart
• SQL Database Modeler
• Erwin Data Modeler

5. Web Scrapping Tools

• Beautiful Soup
• IXML
• MechanicalSoup
• Selenium
• Scrapy

6. Machine Learning & Predictive Analysis Tools

• Matlab
• SPSS
• Julia

7. Deep Learning Tools

• TensorFlow
• PyTorch
• Keras

8. Artificial Intelligence Tools

• Scikit Learn
• Theano
• CNTK
• AutoML
• Google ML Kit

9. Data Visualization

• QlikView
• Zoho Analytics
• Infogram
• D3js
• Datawrapper

10. Frameworks & Libraries

• TensorFlow
• Pandas
• Matplotlib
• Numpy
• Keras

How “Experienced” Professionals Can Start with Data Science?

If you’re a working professional holding an experience in different domains, it doesn’t matter whether the experience is relevant or not, all it requires is you to have a list of required skills and technologies that you should work on.

1. Mathematics: You must be well versed in all of the sections that have been discussed above.

2. Programming Language: However, you can pick any language but it is recommended to pick Python so that you can get more exposure in the data science field.

3. Tools & Frameworks: Here are some of the popular frameworks and tools you would have already gained insight about – Tableau, MS Excel, Power BI, Pycharm, SQL, etc. Here are the Top 10 Python Libraries for Data Science you must definitely have a look at.

4. Additional Curriculum: For best practice, you can enroll in the Complete Data Science Program – Live Course that will help you in enhancing the required skills and will provide the hands-on experience to learn all the required tools and techniques.

To learn in-depth knowledge of Python, you may consider these two courses:

1. Data Analysis with Python
2. Python Programming Foundation – Self Paced

These were the list of tools, frameworks, and languages that are required to work in the data science domain for different positions and hierarchies if you’re a beginner. Next is the list of some soft skills that are also necessary to look after during this phase.

Required Soft Skills for Data Science:

• Communication
• Clarity of business goal
• Team Player
• Adaptability
• Critical Thinker
• Business-Centric Awareness

Now, when you’re all done with the background knowledge on data science, these 7 Tips to Make a Smooth Career Transition to Data Science will help you to transit in a better way.

Tips to Prepare for Data Science Interview

Finally, preparing for the job interview after you’ve all the required skills and a good resource will add bonus points for a smooth transition. We’re sharing below the list of a handful of material that you need to work upon. Let’s check them out:

• Start with building a solid resume by using Resume Builder
• Create your online presence by using an online portfolio builder and start pushing your work/projects (such as GitHub)
• Grind your coding skills for the technical round (as per the company’s standard where you’ll be applying)
• Try solving complex questions and puzzles on different technical topics for fluency
• Always ensure to read about the profile of the company and your JD (job description) as soon as you apply
• Make sure at least some of the responsibilities mentioned in JD match with your bullet knowledge.
• Work on your soft skills to leave good impressions and build confidence