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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 Analyst in 2024: A Complete RoadMap

Yana Khare
Yana Khare Last Updated : 11 Sep, 2024
8 min read

Introduction

The year 2023 has been a pivotal chapter, shaping the landscape of data analysis and insight generation. As we step into the promising horizon of 2024, data analytics beckons with fresh opportunities and evolving challenges. Charting a course through this dynamic terrain requires expertise and a strategic roadmap, a blueprint that navigates the intricacies of data exploration and interpretation. Enter the Learning Path to Become a Data Analyst and Skills Required in 2024. This comprehensive guide equips aspiring analysts with the indispensable skills and knowledge to thrive in this ever-evolving field. Join us as we unravel the layers of this transformative journey, unveiling the key milestones and insights that will shape your voyage toward becoming a proficient data analyst in the year ahead.

In this article, you will discover the essential steps in the data analyst roadmap, learn how to become a data analyst, and explore a comprehensive data analyst course roadmap to guide your journey.

Table of contents

Why Should You Start a Career as a Data Analyst?

In recent years, there has been a surge in the number of people looking for information on becoming data analysts. The role has grown in popularity, which is unsurprising given the vast data we generate today.

Companies in all industries want professionals who can collect data, evaluate it, derive valuable data-driven insights from it, and use those insights to help them address critical business challenges. As a result, there are several reasons why you would choose to work as a data analyst:

  • High demand: Historically, there has been a shortage of skilled data analysts, resulting in a high demand for professionals who can interpret and derive insights from complex data sets. The Bureau of Labor Statistics projects that the employment of data analysts will grow 23% from 2021 to 2031, much faster than the average for all occupations.
  • Competitive salaries: Data analysts often command competitive salaries due to their specialized skills and the increasing value of data-driven decision-making. Data analysts earn a good median annual wage of $88,240.
  • Diverse industry opportunities: Data analysis skills are transferrable across industries. Thus allowing professionals to explore various sectors and work on diverse projects.
  • Impactful insights: Being a data analyst allows one to uncover patterns, trends, and correlations in data, enabling organizations to make decisions that can significantly impact their success.
  • Continuous growth and learning: The field of data analysis is dynamic, requiring individuals to stay updated with the latest tools, techniques, and technologies. Therefore offering continuous learning opportunities.

What is Data Analyst?

Companies in all industries want professionals who can collect data, evaluate it, derive valuable data-driven insights from it, and use those insights to help them address critical business challenges.

A data analyst is a professional who has the technical skills to work with data and the analytical abilities to extract meaningful information and actionable insights from data sets. Their role is to bridge the gap between raw data and informed business decisions by applying statistical methods, programming, data visualization, and problem-solving techniques.

Skills Needed to Become a Data Analyst in 2024

There has never been a better moment to start a career in data analysis. In this essay, I’ll walk you through the entire process of becoming a Data Analyst in 2024. You must master the following skills:

Technical Skills

  • Storytelling with Data: This skills required for Data Analytics revolves around presenting data compellingly and understandably. It involves understanding the audience, structuring information, and using data visualization tools to tell a coherent story.
  • Programming: Proficiency in programming languages like Python, R, SQL, or others is crucial for data manipulation, analysis, and automation. Knowledge of libraries and frameworks for data manipulation and analysis is also beneficial.
  • Exploratory Data Analysis (EDA): This skill involves exploring and understanding data sets using various statistical and visualization techniques. EDA helps in identifying patterns, outliers, and relationships within the data.
  • Basic Statistics: Understanding foundational statistical concepts such as mean, median, standard deviation, probability, hypothesis testing, and regression analysis is essential for interpreting data accurately.

Soft Skills

  • Structured Thinking: The ability to approach problems logically and systematically is crucial. Structured thinking helps break down complex issues into manageable parts, making it easier to analyze and solve problems effectively.
  • Analytical Skills: This involves critical thinking and the ability to analyze information, identify trends, draw conclusions, and make data-driven decisions. Strong analytical skills aid in solving complex problems and deriving valuable insights from data.
  • Communication Skills: Clear communication is critical in presenting findings, explaining complex analyses, and collaborating with team members. This includes spoken communication for discussions and written communication for reports and documentation. Presentation skills are also essential for conveying information effectively.
Data Analyst Skills

Are you feeling overwhelmed? Don’t worry. We’ve put together a 6-month plan to help you learn these abilities. To make things easier, we’ve separated the roadmap into two quarters. This Skills Required For Data Analytics path assumes that you will study for at least 4 hours per day, 5 days per week. If you stick to this strategy, you should be able to:

  • Begin applying for entry-level Data Analyst roles after the first quarter and 
  • full-fledged Data Analyst roles after the second quarter.

Quarter 1: Straighten Out the Basics

In the first quarter, we aim to prepare you for a Data Analytics Internship or even an entry-level Data Analyst job! So here, you must focus on learning three primary data analytics skills: Microsoft Excel and SQL Programming, Storytelling with Data, and EDA using ChatGPT. Now. Let’s check out what you need to learn.

Month 1: Data Exploration using Excel+SQL 

In the first month, focus on the tools that every Data Analyst must know: Microsoft Excel and SQL. These tools will help you with data exploration, the first step in data analysis. 

Under Excel, you should focus on

  • Creating and formatting worksheets
  • Essential functions like Average, Min / Max, Count, etc.
  • Advanced functions like Vlookup, SumIf, CountIf, SumProduct, Concatenate, etc.
  • Pivot tables / Conditional formatting
  • Various types of Charts
  • Performing: Sensitivity Analysis
  • Building Gantt Chart / Financial Statement

Within SQL, learn things like Querying Databases and managing and manipulating data stored in relational databases. For practice, you may do SQL projects like these. This will make you fluent in SQL.

Month 2: Story Telling with Data

You will learn to tell stories with your data in the second month. For this, focus on learning one of these data visualization tools: Tableau, PowerBI, or Qlik Sense. After that, use these tools to analyze and present a given data visually appealing and interactively. You should also learn how to build an interactive Dashboard on topics like:

  • Covid Vaccination Dashboard
  • Cricket World Cup Visualization Dashboard, etc.

Month 3: Exploratory Data Analysis (With ChatGPT)

Apart from that, you will also learn EDA or Exploratory Data Analysis. This is the process of investigating the data to discover hidden patterns. It includes techniques like Univariate / Bivariate Analysis.

With the advent of ChatGPT, tasks like EDA can be done much faster with tools like Code Interpreter. To do this, you only need to provide your dataset to ChatGPT and ask questions like Check for missing values. How do we attribute these missing values with mean or median?  What visualization is most suitable for representing a given dataset?” or “Checking for outliers in a dataset?”. 

You can enhance your EDA questioning skills by learning Prompt Engineering. This will help you write effective prompts to elicit desired information from LLMs like ChatGPT.   

Soft Skills to Focus in Quarter 1

As I said at the beginning, soft skills are equally important as technical skills and Data Analyst Learning Path for a Data Analyst role. So, in this entire first quarter, you must also sharpen your Communication and Analytical Skills. Specific to communication skills, you may start writing blogs or making YouTube videos to share your learnings. This improves your writing as well as spoken skills. Additionally, for Analytical skills, you must solve various Logical Reasoning and Data Interpretation problems. 

Things to do after Quarter 1

At the end of this first quarter, you will have a solid understanding of drawing inferences from data and building stories around them. You know what, at this point, you may start applying for Internships and even entry-level Data Analyst roles.

You will need to create a Resume, Cover Letter, and LinkedIn account by now. And given you know ChatGPT and the art of Prompt Engineering, you may do all that in minutes.

We have created a series of videos on how to do this. You may refer to them as well. 

Moving on to the Second Quarter. 

Also Read: Top 10 SQL Projects for Data Analysis

Quarter 2: Learning the Essential Data Analysis Skills

We aim to prepare you for full-fledged data analyst roles in the second quarter. So our focus should be on strengthening our subject knowledge. For a good Data Analyst, in depth knowledge of mathematics, statistics, or programming for that matter, is a must. These skills give a solid technical foundation to perform Exploratory Data Analysis, and Basic Statistics. 

Month 4: Learning Python and Basic Statistics

The first thing we will learn in month 4 is a general-purpose programming language like Python/R. Now, Python is a popular choice among Data Analysts. This is because: 

  • It is easy to learn
  • It has a wide range of applications
  • And a handful of Libraries like Pandas, NumPy, Matplotlib, and Seaborn that make Data Analysis easy. 

Basic Statistics follows this. Under statistics, focus on:

Month 5: End-to-End Projects

This is the second last month of our journey. This month is all about practice. You have learned all the skills you need to know. So what’s next? Next is end-to-end projects, where you solve a real-world problem like a real Data Analyst Learning Path does.

Projects also give you the much-needed platform to practice whatever you have learned, revise your skills, and become a better data analyst. This month, these are the projects you may do. Apart from this, you will also practice Data Analytics Interview Questions. Here’s a video we have done on this.

Month 6: Basic Machine Learning Algorithms

Finally, you should also have basic knowledge of a few simple Machine Learning Algorithms. Namely, Linear Regression, Logistic Regression, Decision Tree, K-Nearest Neighbour, etc.

Believe it or not, these beginner-level ML algorithms can be applied to almost any data problem.

Soft Skills to Focus in Quarter 2

The soft skill we will focus on in this second quarter is: structured thinking. A great way to do so is by practicing Guesstimation and going through various Case Studies. With structured thinking, you can learn how Data Analysts work and think. 

Another skill to learn is Mind Mapping, which will help you chalk out your thinking structure.

Getting a job after Quarter 2

Guys, within this quarter, you may begin applying for full-fledged Data Analyst roles in the industry. Earlier, we told you how to create a LinkedIn profile, resume, and cover letter. Update them as per the job experience you have. 

Now, the next step is getting a job! We have made videos on landing a job in the Data Tech domain. These may help you get a callback and crack interviews with the help of Generative AI.

how to become data analyst roadmap

Conclusion

Becoming a proficient data analyst in 2024 is an intricate yet rewarding path, promising opportunities amidst evolving challenges. As we conclude this comprehensive guide, it’s evident that the demand for skilled data analysts continues to surge, creating a landscape ripe with prospects for those equipped with the right expertise. As you navigate this transformative journey, embracing the complexities and challenges while staying committed to learning and growth will pave the way for a fulfilling career as a data analyst in the year ahead.

Hope you like the article! To become a data analyst, follow a structured data analyst roadmap. Explore essential skills through a comprehensive data analyst course roadmap for success. Also there is roadmap to become for data scientist Check here.

Q1. What are the 5 types of data analytics?

A. The five types of data analytics are Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics, and Exploratory Analytics.

Q2. What is data analytics useful for?

A. Data analytics is useful for extracting insights, making informed decisions, improving efficiency, and identifying trends/patterns within large datasets.

Q3. Is data analytics a good career?

A. Yes, data analytics is considered a promising career with high demand and growth opportunities due to the increasing reliance on data-driven decision-making across industries.

Q4. What is a job of data analytics?

A. The job of a data analyst involves collecting, cleaning, and analyzing data to uncover trends, patterns, and insights. They also develop reports, dashboards, and visualizations to communicate findings and aid decision-making processes within organizations.

Advanced Statisticsbasic statisticalcareer guidedata analysisData Analystdata analyst skillsEDAhow to become a data analyst 2024how to become data analystLearning Path to Become a Data AnalystMicrosoft Excel
Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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Responses From Readers

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Saeed.J
Saeed.J

Hello, although I don't have much knowledge about data science, but the article you wrote was very motivating and encouraged me to take a step in this direction. Thank you for sharing your knowledge and experience with us.

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Juliet iwemah
Juliet iwemah

This was the best I have seen today .. thanks so very much

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Tejas
Tejas

Hats off to Sanjana Rajpan and Analytics Vidya team for such a detailed and well-structured article.

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Is Data Analyst a Good Career?
Is Data Analyst a Good Career?
Data Analyst Job Description and Responsibilities
Data Analyst Job Description and Responsibilities

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