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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

How to Go For a Data Science Career Change at 30? 

A
Analytics Vidhya Last Updated : 14 Sep, 2023
7 min read

Making a career change in data science at 30 isn’t only possible but very unusual. Data science offers exciting possibilities for those with the right skills and mindset, and age must not be a barrier to pursuing your dreams. This guide will explore the steps and strategies for effectively transitioning into a data science profession, irrespective of your previous professional background. Whether you are in your 30s or beyond, the world of data science is open to everyone, and this guide will assist you in navigating the route to a rewarding profession. 

Table of contents

Is it Possible to Go for a Data Science Career Change at 30? 

Source: Tenor

Although it isn’t always possible, it is quite feasible to embark on a data science career change at 30 or even later. The field of data science is characterized by its sincerity to people from different backgrounds, and it values skills and aptitude over age. Here’s why:

Inclusivity in Data Science

The data science profession welcomes experts from diverse fields. Your previous experience and knowledge can be a plus point, as they offer a unique perspective and area of expertise that can be carried out in data analysis and problem-solving.

Demand for Data Scientists

The demand for data scientists keeps growing throughout industries. Companies of all sizes are searching for data-driven insights to make knowledgeable decisions. This high demand relates to a willingness to hire applicants from different fields of experience.

Learning Opportunities

The resources for studying data science are abundant and accessible. Online courses, boot camps, and degree programs cater to individuals at different stages of their careers. You can choose a suitable path that suits your goals and ambitions.

Transferable Skills

Many skills from your previous profession can be used in a data science profession. For instance, project management, problem-solving, and communication skills are valuable in a data scientist role.

Networking

Building a community in the data science network can be useful in your career transition. Attending meetings and online forums lets you connect with experts who can provide guidance and possibilities.

Continuous Learning Culture

Data science is an area that encourages continuous learning. Being adaptable and open to learning new skills is highly valued, making it easy to change careers.

Assess Your Readiness Before Changing Career at 30

Assessing your readiness for a career change into data science involves various important aspects. 

Why choose Data Science
Source: DELL EMC Study
  • Firstly, evaluate your talents and knowledge in detail. While having experience in programming and data analysis, there’s a lot to learn, especially in areas like machine learning, data analysis, and data visualization.
  • Identifying transferable skills has been another important aspect of readiness evaluation. These skills may be used in dealing with complex data projects and working effectively with data science teams.
  • One of the most essential components of readiness is adopting a growth mindset. Recognizing that learning and growth are ongoing processes, you should be prepared to embrace challenges, setbacks, and the need for continuous skill development. A growth mindset allows you to view obstacles as possibilities for learning and improvement, which is important in a field as dynamic as data science.

Acquiring Essential Data Science Skills

Acquiring essential data science skills includes formal education, self-learning, and building a strong portfolio. 

Formal Education and Self-Learning

  • Formal Education: A formal education in data science, such as a Master’s in Data Science or related fields like Statistics or Computer Science, can provide a  comprehensive education. It’s an outstanding choice if you prefer formal education and have the time and resources for a degree program.
  • Self-Learning: Self-learning through online publications, books, and tutorials is a flexible and cost-effective approach. This approach is appropriate for the ones searching to acquire skills while working at their current job.

Start your career transition today, with our BlackBelt Plus program. The course has helped several learners change their career from diverse fields like Mechanical Engineering, Civil Engineering, System Engineering, Marketing, Data Analyst, Software Engineering and many more.

  • Data Science Specializations: Enroll in full-time data science specializations courses. 
  • Certifications: Consider certifications just like the “Certified Data Scientist” (CDS), like certifications like the one provided by Microsoft (Microsoft Certified: Azure Data Scientist Associate) or Google (Google Data Analytics Professional Certificate).
  • Books: Explore data science books, including “Python for Data Analysis” by Wes McKinney, “Introduction to Statistical Learning” by Gareth James, and “Deep Learning” by Ian Goodfellow, for professional expertise and knowledge.

Building a Portfolio

  • Projects: Practical experience is essential. Work on data science tasks that interest or align with your career goals. These include personal initiatives, contributions to open-source projects, or freelance work.
  • Kaggle: Participate in data science competitions on Kaggle. This is the best platform to enhance your skills and showcase your problem-solving abilities.
  • Blogs and Publications: Write blogs or articles about data science topics, challenges, and more. Share them on Platforms like Medium or LinkedIn to demonstrate your expertise.

Also Read: How to make an impressive Data Science Portfolio?

Leveraging Your Previous Experience

Leveraging Your Previous Experience
Source: Yahoo

Leveraging your previous experience in your data science career transition can be a precious resource. 

Highlighting Unique Strengths

  • Problem-Solving Skills: Emphasize your ability to handle complex problems. Data science regularly involves tackling complex problems, and your problem-solving skills from your preceding career can be advantageous.
  • Project Management: If you’ve got experience managing projects, highlight your organizational and project control skills. Data science projects often require planning, execution, and delivery, making project management skills highly applicable.

Identifying Industries

  • Healthcare: If you have a background in healthcare, your skills can be treasured in roles related to healthcare data analysis, predictive modeling for patient outcomes, or scientific studies.
  • Finance: Financial institutions rely heavily on data for risk assessment, fraud detection, and funding strategies. Your previous experience in finance can be wonderful in these aspects.
  • Marketing: Marketing analytics is a developing field, and your knowledge of customer conduct and marketing strategies can be applied to roles involving customer segmentation, campaign optimization, and market analysis.
  • Engineering: Engineers often possess strong analytical and problem-solving skills. These abilities may be leveraged in data science or machine learning roles, wherein optimizing algorithms and data pipelines is important.

Networking

  • Online Communities: Join online data science communities and boards wherein you can communicate with professionals in the field. Engage in discussions, seek recommendations, and share your journey. 
  • Meetups and Conferences: Attend your area’s data science meetups, conferences, and workshops. These events provide possibilities to communicate with specialists, discover industry trends, and discover inspiring mentors.
  • LinkedIn: Optimize your LinkedIn profile to showcase your transition into data science. Connect with data science professionals, comply with relevant companies, and participate in data science groups and discussions.

Navigating the Job Market

Navigating the data science job market requires careful preparation and effective strategies. 

Crafting a Data Science Resume and Cover Letter

  • Tailor Your Resume: Customize your resume to match the data science roles you are applying for. Add your relevant skills, experience, and projects that reveal your talents.
  • Achievements: Use metrics to show your impact in previous roles. For example, mention how you improved efficiency or increased revenue by data-driven insights.
  • Technical Skills: Include a  section for technical talents, programming languages (e.g., Python), machine learning, data visualization tools, and database management systems.
  • Projects: Describe data science tasks you’ve worked on, emphasizing the problem-solving approach, data preprocessing, modeling techniques used, and outcomes achieved.
  • Cover Letter: Write a compelling cover letter that explains your passion for data science, highlights your relevant skills and experiences, and suggests why you’re the best fit for this role.

 Preparing for Interviews and Technical Assessments

  • Technical Knowledge: Review and practice your technical skills, such as coding in Python, machine learning algorithms, and data manipulation. Be ready to discuss your projects and the methodologies you applied.
  • Behavioral Interviews: Prepare for behavioral questions investigating your problem-solving abilities, teamwork, and communication skills. Structure your responses using the STAR (Situation, Task, Action, Result) approach.
  • Case Studies and Technical Assessments: Some interviews may include case studies or technical assessments. Practice similar things and explore online resources or guides to enhance your skills.
  • Industry-Specific Knowledge: If you are transitioning into a selected industry (e.g., finance or healthcare), study industry-specific trends and challenges

 Leveraging Online Job Platforms and Professional Networks

  • LinkedIn: Update your LinkedIn profile to reflect your data science journey. Connect with experts in the domain, follow relevant companies, and participate in data science groups and discussions.
  • Online Job Platforms: Use job search websites like LinkedIn Jobs, Indeed, Glassdoor, and specialized data science job search websites like Kaggle Jobs or DataJobs to find relevant positions.
  • Professional Networks: Attend data science meetups, conferences, and workshops both offline and online. These events provide networking opportunities and job leads.
  • Leverage Alumni Networks: If you attended a data science program, step into alumni networks for job referrals and advice.
  • Cold Outreach: Don’t hesitate to contact professionals in the field for informational interviews. Express your interest in data science and take advice on searching for a job.

Conclusion

In the end, embarking on a career change in data science at 30 or beyond is possible and filled with opportunities for personal and professional growth. Individuals can successfully transition into this dynamic field with determination, a dedication to continuous learning, and a strategic approach. Kickstart your journey with our BlackBelt Plus Program!

Frequently Asked Questions

Q1. Is 30 too late for data science?

A. No, 30 is not too late for a career in data science. Many professionals start their data science journey in their 30s and succeed by building on their existing skills and experiences.

Q2. Can a 30-year-old become a data analyst?

A. Absolutely, a 30-year-old can become a data analyst. Age is not a barrier in entering this field. Focus on acquiring the necessary skills and gaining relevant experience.

Q3. Is 30 too late for a career change?

A. No, 30 is not too late for a career change. Many people switch careers in their 30s, and with dedication and upskilling, it’s possible to transition into data science or other fields.

Q4. How do I become a data scientist in my 30s?

A. To become a data scientist in your 30s, start by learning programming languages like Python and R, gaining expertise in statistics and machine learning, and building a strong portfolio of projects. Consider online courses and networking to enhance your career prospects.

A
Analytics Vidhya

Analytics Vidhya Content team

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