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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 With No Experience?

A
Analytics Vidhya Last Updated : 08 Jul, 2023
6 min read

Introduction

Did you know that entry-level data analysts can earn up to $49,092 annually? In today’s data-driven world, data analytics careers span diverse industries, offering numerous pathways to enter this rapidly-growing field. Data is the primary decision-making tool for every organization. Analytics is an essential aspect of strategic planning across all sectors. This article aims to answer a common question among freshers – How to become a data analyst with no experience!

Table of contents

Is it Possible to Become a Data Analyst With No Experience?

Absolutely! You can pursue a data analyst role with no experience by obtaining the necessary qualifications. Several factors make the data job market accessible to beginners:

  1. Lack of data expertise: The demand for data professionals surpasses the current supply, creating opportunities for newcomers to enter the field.
  2. Emphasis on transferable skills: Data analytics values skills that can be applied from other domains, allowing individuals to leverage their existing abilities.
  3. Rapid market growth: The data market has witnessed exponential growth, increasing the need for skilled professionals across industries.

Hiring data experts becomes a top priority as businesses rely on data-driven strategies. By investing effort, embracing growth, and accessing appropriate training resources, individuals can acquire the expertise needed to thrive in this dynamic field.

How to Become a Data Analyst With No Experience?

Here is your step-wise guide to get a data analyst job with no experience:

  1. Gain Relevant Skills
  2. Master Data Tools
  3. Creating a Professional Portfolio
  4. Networking and Seeking Internship Opportunities
  5. Leveraging Online Learning Platforms
  6. Joining Professional Data Analysis Communities
  7. Showcasing Your Skills Through Personal Projects
  8. Tailoring Your Resume and Cover Letter
  9. Preparing for Data Analyst Interviews

1. Gain Relevant Skills 

It’s not necessarily essential to be a data analyst to have a degree in a relevant subject; however, having one in statistics, mathematics, or computer science might be helpful. You can sign up for in-person training sessions, watch video tutorials, or take online courses to increase your data expertise. Learn Python libraries like Matplotlib and Seaborn and data visualization applications like Tableau, Power BI, and others. Invest time in understanding language syntax, data types, and packages associated with programming languages.

2. Master Data Tools

Real-data projects can give you practical experience while instructing you on how to use data in practical settings. You may participate in existing projects or create your own by utilizing some of the freely available public data sets and building your project around those. Experiment with tools like Excel for data handling, SQL for database querying, and statistical software like SAS or SPSS.

Alt text: Tools for how to become a data analyst with no experience
Source: Monkeylearn

Helpful Resources –

  1. 10 Best Data Analytics Projects with Source Codes
  2. Beginners Guide on SQL
  3. Learn MS Excel Online For Free
  4. Step-by-Step Guide to Becoming a Data Analyst in 2023

3. Creating a Professional Portfolio

A portfolio of work can help you get a job because it builds authenticity and displays the projects you participated in in the past. Build a portfolio that showcases your skills by documenting your projects and their results. The portfolio you create might be simple, but it’s a good idea to incorporate a biography portion that highlights your fondness for data analytics and your education and experience. 

Data Analyst Fresher Portfolio
Source: BeamJobs

4. Networking and Seeking Internship Opportunities

Networking can result in insightful connections, mentorship possibilities, and even helpful internships. Explore reaching out to the connections you’ve developed from your projects, classes, and self-study. Recruitment websites are a wonderful resource for discovering prospective job opportunities. 

How to become data analyst with no experience through networking
Source: Springboard

5. Leveraging Online Learning Platforms

The best way to upskill in data analytics field is through a professional online course. A professional certificate from an accredited institution might help you show businesses you have the education needed for the job. The most effective courses focus on projects and give students access to resources for career services. Our Blackbelt Plus Program offers a one-stop solution to all your needs. From guided projects to one-on-one sessions with expert mentors, you get all the resources to become a data analyst!

6. Joining Professional Data Analysis Communities

You may establish connections with business leaders, possible mentors, and recruiters by engaging in discussions, attending events, and interacting with other members. You can build a strong professional standing within the sector by actively participating in such networks. Additionally, it improves your exposure and increases the likelihood that companies would approach you directly about job openings.

Connect, Learn, Thrive: Join the Analytics Vidhya’s Data Community Today!

7. Showcasing Your Skills Through Personal Projects

Suppose you’re completely new to the field of data analytics. In that case, it’s essential to establish a correlation between the skill set of your previous projects and skills for your new job opportunities. Gather relevant data, assess it, and draw insightful inferences. Create a methodology report, then concisely convey what you have learned.

8. Tailoring Your Resume and Cover Letter

Having an all-around resume is a good idea if you wish to apply for multiple jobs. However, tailor your CV if you have a particular position in mind. Edit your resume to emphasize skills that are essential to the position’s requirements.  Wherever it is possible, highlight your technical skills and projects and provide numbers for your accomplishments. 

9. Preparing for Data Analyst Interviews

The interview gives the hiring managers a better understanding your skills and prospective match for the position. Prepare your primary concepts, algorithms, and statistical methods well. Also, be prepared to showcase your skills for problem-solving while working with real-world datasets. You may stand out in the interview by showcasing your understanding of the latest innovations and how they affect data analysis.

Entry Level Data Analyst Salary

According to Glassdoor, the average entry-level data analyst’s salary would be around $51,965 annually in the US and ₹5,19,033 per year in India. Your location (or the location of the work) and the industry you’re entering will determine how much money you may expect to make with your first data analyst position. One of the most significant factors that could affect your salary is the amount of experience you have.

Entry-Level Data Analyst Jobs You Can Get Without Experience

Here is a list of entry-level jobs you can get with no experience: 

  • Junior data analyst
  • Quality assurance analyst
  • Marketing and sales data analyst
  • Data associate
  • Research analyst
  • Data quality analyst

Conclusion

A data analyst employs data to address issues, find solutions, and suggest techniques for enhancement. Objectives of the job include looking at patterns and using present data to fuel projections and future approaches. Putting your foot in the door as a data analyst is only the beginning. Analytics vidhya offers various courses to help freshers build their skills.

FAQs

Q1. How do I start a data analyst for beginners?

A. Beginners can start as a data analyst by learning the fundamentals of data analysis. Sign up for online courses to learn the skill set for data analysis. 

Q2. What qualifications do I need to be a data analyst?

A. Some data analysts might have a graduate degree in subjects mathematics, statistics and computer science. It is not essential to have a degree in specific subjects; with the right skill set, any individual can be a data analyst.

Q3. Can I join a data analyst as a fresher?

A. Yes, one can be a data analyst as a fresher with the proper skill set and expertise.

Q4. How do I become a data analyst from zero?

A. Start with small steps, such as learning the fundamentals of data analysis, joining an online course to learn the tools used, making a portfolio, networking and applying for various job roles. You can consider signing up for our Blackbelt program to gain the relevant skills.

A
Analytics Vidhya

Analytics Vidhya Content team

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