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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 Research Analyst? Description, Skills, and Salary

Nitika Sharma
Nitika Sharma Last Updated : 22 Jan, 2024
5 min read

Introduction 

A research analyst plays a pivotal role in organizations across various industries, offering insights that drive informed decisions. They are the data detectives, uncovering valuable information and transforming it into actionable intelligence. But how can you embark on this exciting career path? This comprehensive guide will walk you through the journey to becoming a research analyst.

Source: Infomineo

Table of contents

What is a Research Analyst?

A research analyst is a professional responsible for collecting, analyzing, interpreting, and presenting data to support informed decision-making within an organization or industry. Their primary role is to conduct research, often focused on specific sectors such as finance, market trends, healthcare, technology, or social sciences, and provide insights and recommendations based on their findings.

Research Analyst
Source: WallStreetMojo

Types of Research Analysts

  • Market Research Analyst : A market research analyst helps businesses understand their customers, competitors, and market trends. They use surveys, interviews, and data analysis to find out what people want, need, and buy.
  • Operation Research Analyst: An operation research analyst helps organizations solve complex problems and make better decisions. They use mathematical models, simulations, and optimization techniques to improve efficiency, productivity, and quality.
  • Economic Research Analyst: An economic research analyst studies how people, businesses, and governments use resources and interact with each other. They use economic theories, data, and statistics to analyze issues such as inflation, unemployment, trade, and growth.
  • Financial Analyst: A financial analyst evaluates the performance and potential of companies, industries, and investments. They use financial statements, ratios, and forecasts to provide advice and recommendations to investors, managers, and clients.
  • Equity Research Analyst: An equity research analyst analyzes the financial health and value of publicly traded companies. They use fundamental and technical analysis, industry research, and market trends to produce reports and ratings for stocks.

Research Analyst Salary

The compensation for research analysts is dynamic, influenced by factors like experience and industry. On average, research analysts earn a median income of $62,989 annually, surpassing the typical American salary of approximately $44,000.

Among the sectors offering lucrative prospects for research analysts are banking and financial services, healthcare, pharmaceuticals, manufacturing, IT, and government agencies.

For those seeking higher earnings, roles in IT companies or hospitals can be rewarding. Notably, in India, research analysts command an average annual salary of 5,00,000 INR, reflecting the global demand for their analytical expertise.

Skills Required 

Research analysts are crucial in helping organizations make informed decisions, whether evaluating investment opportunities, understanding customer preferences, or assessing market dynamics. Key responsibilities of a research analyst include:

  1. Data Collection: Gathering relevant data from various sources, including surveys, interviews, databases, market reports, and online resources.
  2. Data Analysis: Applying statistical and analytical techniques to process and interpret data, identify trends, correlations, and patterns, and draw meaningful conclusions.
  3. Report Generation: Communicating research findings through reports, presentations, and visualizations to help stakeholders make informed decisions.
  4. Market and Industry Research: Monitoring and analyzing market conditions, competition, consumer behavior, and industry trends to provide actionable insights.
  5. Financial Analysis: To support investment decisions, assessing financial data, including company financial statements and economic indicators.
  6. Risk Assessment: Evaluating potential risks and opportunities associated with various business strategies, investments, or projects.
  7. Specializations: Research analysts may specialize in areas such as equity research, market research, healthcare analysis, data analysis, policy analysis, or environmental research, among others.
  8. Continuous Learning: Staying up-to-date with the latest research methods, tools, and industry-specific knowledge to enhance their analytical skills.

How to Become a Research Analyst? 

Time needed: 10 minutes

You can follow these steps to become a research analyst in 2023: 

  1. Educational Background and Qualifications

    The foundation of a research analyst’s career often begins with the right education. Explore the diverse educational backgrounds that can lead to success in this field, from degrees in finance and economics to data science and market research.

  2. Essential Skills

    To excel in this career certain skills are indispensable. We’ll delve into the key competencies, including data analysis, critical thinking, communication, and a knack for staying updated with industry trends.

  3. Gaining Practical Experience

    Discover the importance of gaining hands-on experience through internships, freelance work, or entry-level positions. Practical exposure is your stepping stone to mastering the craft.

  4. Networking and Career Advancement

    Networking is vital for career growth. Learn how to build a professional network within the research community and explore avenues for career advancement, including certifications and further education.

  5. Industry Focus and Specializations

    Explore the exciting realms of finance, healthcare, technology, and beyond. Discover how specialization can open doors to unique career opportunities.

Role and Responsibilities

These are some of the key roles and responsibilities of a research analyst in more detail:

  • Use of Mathematical Models: They employ mathematical models and quantitative techniques to derive objective answers and insights from historical data. These models can range from simple statistical analyses to complex predictive algorithms.
  • Recommendation for Investment: They provide well-founded buy or sell recommendations to money managers, brokerages, or investors. Their analysis is based on a deep understanding of market trends, financial indicators, and economic factors.
  • Data Analysis: They utilize statistical software to rigorously analyze data, identify patterns, and draw meaningful conclusions. This data-driven approach ensures that recommendations are backed by solid evidence.
  • Trend Identification: They are skilled in recognizing short-term and long-term market trends. They use their expertise to make informed predictions and offer strategies to capitalize on these trends.
  • Data Visualization: They create presentations enriched with charts and graphs, making complex data more digestible for higher-ups and management. These visuals facilitate decision-making by providing a clear overview of findings.
  • Strategic Recommendations: They go beyond data analysis; they develop actionable recommendations for the future of the business. These recommendations may involve changes in investment strategies, portfolio adjustments, or risk mitigation.
  • Strategy Evaluation: After implementing recommendations, they assess the effectiveness of the strategies. They continuously monitor performance, analyze outcomes, and adjust as needed to optimize results.

Is Research Analyst a Good Career? 

Absolutely, working as a research analyst is a highly rewarding career choice for individuals passionate about providing valuable insights that empower companies to outperform their competitors. Research analysts typically enjoy a high salary, along with numerous perks and benefits. What makes this career even more appealing is its versatility, extending beyond the finance industry to encompass various sectors. Here are a few common types of research analysts in different sectors:

Buy-side

  • Typically employed by asset management companies and institutional investors.
  • Responsibilities include assessing the potential of investments and how well they align with investment strategies.
  • Vital in guiding investment decisions to maximize returns.

Sell-side

  • Commonly found in brokerage firms and companies managing individual customer accounts.
  • These analysts provide recommendations for buying, selling, or holding stocks.
  • Their research and analysis help clients make informed investment decisions.

Conclusion

Becoming a research analyst is a journey that demands a blend of skills, knowledge, and determination. It’s a profession that offers the opportunity to unearth valuable insights and drive strategic decisions. To embark on this path, one must acquire the right qualifications, hone analytical skills, and stay updated with industry trends.

As you aspire to become a research analyst, remember that continuous learning and hands-on experience are key. Whether your passion lies in finance, healthcare, technology, or any other field, the analytical prowess you develop will be invaluable.

To accelerate your journey and equip yourself with the latest tools and techniques, consider enrolling in Analytics Vidhya’s Blackbelt Plus program. It’s a gateway to mastering the art of research and data analysis, making you a sought-after expert in this dynamic field.

Frequently Asked Questions

Q1. What qualifications are required for a research analyst? 

A. Research analysts typically need a bachelor’s degree in finance, economics, or data science. Advanced degrees like an MBA or a CFA designation can enhance their qualifications.

Q2. What does a research analyst do? 

A. Research analysts collect and analyze data to provide insights and recommendations for decision-making. They work across various industries, specializing in finance, healthcare, or market research.

Q3. Do you need a CFA to be a research analyst? 

A. While not mandatory, a CFA (Chartered Financial Analyst) designation is valuable for research analysts, especially in finance. Many professionals pursue a CFA to boost their qualifications and career prospects.

Q4. What is the average salary for an Equity Research Analyst in India? 

A.In India, the average salary for an Equity Research Analyst is ₹8,00,000 per year. Additional cash compensation can range from ₹71,500 to ₹4,76,858, depending on experience and performance.

Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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