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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 Become a Data Strategist in 2025?

A
Analytics Vidhya Last Updated : 12 Dec, 2024
5 min read

Introduction

Are you fascinated by the real-world challenges that data holds? Are you excited to uncover hidden insights and transform businesses with the power of information? If yes, becoming a data strategist is your right career path. Imagine being the hero of an organization, armed with the ability to turn massive data sets into game-changing strategies. You’ll be the go-to person, unlocking secrets and guiding businesses toward unprecedented success. In this article, we will discuss how to become a data strategist!

Table of contents

What is Data Strategist?

A data strategist is a skilled professional who plays a pivotal role in shaping data-driven organizational decisions. They excel at collaborating with stakeholders, gaining valuable insights into requirements and data sources, and crafting innovative data-driven solutions. As the importance of data continues to surge, companies are recognizing the indispensable value of data strategists for efficient and effective data management. With their expertise, businesses can confidently navigate the sea of data, unlocking the untapped potential for growth and success.

What is Data Strategist? 
Source: Dataroots

Why Do You Need a Data Strategist?

  • Aid in data-driven decision-making. 
  • Identify the opportunities capable of providing maximum value from data assets.
  • Aid in strategic planning to reach the organization’s vision and objectives.  
  • Integrate data systems and technologies to minimize inefficiencies. 
  • Data strategist is concerned with data-related challenges such as quality, data security and scalability. 
: Data-driven decision making
Source: Reveal

Job Role

The data strategist job description is as follows: 

  • Define the use cases for marketing data activation in the digital sector 
  • Understand the consumer and professional ecosystem and data model, including transactional, marketing and commerce data
  • Interact actively in works concerning data architecture designs and oversee their management 
  • Handle the project timelines
  • Maintain cross-functionality interaction and action across the departments 
  • Refine the institutional data capacity by data collection, analysis and dissemination of practices
  • Contribute to efficient designing of metrics 
  • Work on data visualization and analysis 
  • Utilize dashboard tools, outline processes, visualization of complex concepts, and generate business cases. 
  • Aid in strategic decision making 

Skills Required

Here are the skills required to become a data strategist:

Technical Skills

  • A Bachelor’s degree in a quantitative discipline or another related field is necessary. A Master’s degree is preferable. 
  • Professional working experience in domains like Information technology, digital marketing, data governance, data strategy, quantitative and qualitative research methods and statistics or data analysis.
  • Creative visualization using Adobe Creative Suite 
  • Hands-on experience on dashboarding tools or business intelligence platforms such as Tableau or Power BI, SQL Server
  • Should be able to obtain security clearance and submit portfolio and work samples 
  • Experience working on Snowflake, DOMO, Salesforce Marketing Cloud, BlueConic Customer Data Platform
  • Familiar with agile methodology 
  • Ability to develop and implement data strategies 

Soft Skills 

  • Ability to handle multiple assignments with exhibited attention to detail 
  • Client-facing and communication 
  • An analytical and problem-solving approach 
  • Communication and interpersonal skills 
  • Adaptable to diverse culture 
  • Excellent project planning and management skills 

Steps to Become A Data Strategist

Time needed: 10 minutes

Here are some simple steps to build a career as a Data Strategist

  1. Education

    Start by obtaining an education degree in a relevant field, such as statistics, mathematics, computer science, business administration, or any other related area. A solid educational foundation will lay the groundwork for your data strategist career.

  2. Gain Skills

    Acquire essential technical skills in statistics, business analysis, data visualization, and other data platforms. Familiarize yourself with various tools and technologies that are commonly used in the data industry.

  3. Practise More

    Familiarize yourself with data strategy frameworks, best practices, and methodologies. Understand the principles of data governance, privacy, security, compliance, and data quality management. These aspects are vital for effective data management.

  4. Practical Experience

    Gain practical experience through internships or entry-level positions. Working under the guidance of experienced professionals or mentors will help you develop and demonstrate your skills.

  5. Build a Portfolio

    Build a portfolio showcasing your work and projects. Constantly seek opportunities to expand your knowledge and skills. Curate and present your projects effectively, and consider upskilling or pursuing an advanced degree to stay ahead.

  6. Network

    Network and connect with professionals in the data field. Building relationships with experienced individuals can provide valuable insights, mentorship and keep you updated on industry trends and opportunities.

  7. Apply for Jobs

    As you gain technical and soft skills expertise, actively seek job opportunities that align with your career goals. When applying for a job, highlight how you can contribute to the organization and fit into its data-driven culture. Emphasize your problem-solving abilities and how you can effectively analyze and interpret data to make data-driven decisions.

Salary

he salary of a data strategist can vary depending on factors such as location, experience, and the size of the organization. The strategist’s salary in India is about INR 1,05,000 per month and about $113,957 per year in USA. Senior-level data strategists with extensive experience and expertise may command higher salaries.

Conclusion

The emerging role of data strategists in today’s digital landscape is gaining importance as organizations recognize the value of data-driven decision-making. Our Certified AI and ML Blackbelt Plus program provides comprehensive training in technical skills, statistics, and data strategy frameworks, empowering participants to tackle real-world challenges effectively. Our learners stand out in the job market, equipped to guide organizations towards data-driven success and a promising future. As data strategists, they play a pivotal role in driving business growth through data-powered insights and decisions.

Frequently Asked Questions

Q1. What do you need to be a data strategist?

A. Hands-on experience, technical and analytical skills, strategic thinking, decision-making abilities and business expertise are some of the key requirements to be a data strategist. 

Q2. What does a data strategist do?

A. Data strategist aids companies in achieving their goal through data analysis, interpretation, strategic handling of work and aiding in decision-making. They ease companies to leverage their data.

Q3. What is the difference between a data analyst and a data strategist?

A. Data analyst is concerned with analysis and gaining insights into data, while strategists ensure strategic data usage. Data analysis ensures tactical analysis and reporting, while strategists focus on defining data strategies, governance, collaboration and decision-making. 

Q4. How do I become a data expert?

A. Becoming a data expert requires gaining a strong foundation, pursuing quality education, developing technical skills, gaining experience and constantly seeking development and growth.

A
Analytics Vidhya

Analytics Vidhya Content team

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