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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

Data Analyst Job Description and Responsibilities

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Analytics Vidhya Last Updated : 16 Jul, 2023
7 min read

Introduction

The global data analytics market is expected to reach US $132,903 million at a CAGR of 28.9% by 2026. While data has become a powerhouse for businesses globally, it is fair enough to launch your career as a data analyst. A data analyst job description demands proficiency in gathering, cleaning, tweaking, and translating data. If you plan to step forward in this field, you may need to know more than just the data analytics job description. In this article, we will discuss data analyst roles and responsibilities and the qualifications expected of a candidate to get the job.

Table of contents

Who is a Data Analyst?

A data analyst gathers, interprets, and analyzes large data sets to uncover valuable insights and trends. They use statistical and analytical techniques to examine data, identify patterns, and draw meaningful conclusions. Data analysts are crucial in helping businesses and organizations make informed decisions and develop effective strategies. They use tools like spreadsheets, statistical software, and programming languages to work with diverse data sources, such as sales figures, customer demographics, website traffic, and social media engagement. With their data analysis, visualization, and reporting expertise, data analysts contribute to improving business performance and driving data-driven decision-making.

Data Analyst Job Description

Sample Data Analyst Job Description

Source: Velvet Jobs

Key Responsibilities of a Data Analyst

The critical data analyst responsibilities typically involve collecting, analyzing, and interpreting data to generate actionable insights and foster decision-making processes. Now, the duties of a data analyst’s job description can vary based on specifics such as industry, company, and the role itself.

In the lens, here are the five data analyst roles and responsibilities that come in handy in different contexts:

1. Collecting And Analyzing Data

The data analyst role implies gathering data from different sources, which includes databases, spreadsheets, and APIs. An analyst is expected to ensure data accuracy and consistency. Moreover, it may also include transforming data to make it more workable for analysis.

2. Cleaning And Preprocessing Data

Before conducting analysis, data analysts are often required to clean and preprocess raw data to ensure it is effective and suitable for analysis. It is also essential to ensure proficiency in the techniques employed for data cleaning, such as handling missing data, performing data validation, and dealing with outliers.

3. Conducting Data Exploration And Visualization

Data analyst job description often highlights proficiency in statistical techniques and data visualization tools as a must, provided it enables them to analyze and interpret data. Performing data exploration and visualization is integral to identifying patterns and deriving meaningful insights within the data. Hence, a data analyst must be good with tools like Excel, SQL, or programming languages like Python or R.

While it goes without saying, the data analyst job description emphasizes tasks such as scrutinizing the numbers and searching for patterns, trends, and correlations as a core responsibility of the data analyst meaning. With statistical methods and analytical techniques, the professional must be well-versed in interpretation techniques to extract valuable insights.

5. Creating Reports And Presentations

The data analyst role assists in problem-solving by bringing data-driven insights and recommendations. Data analysts work closely with decision-makers and stakeholders to understand their requirements and help them make informed decisions based on the data analysis. They provide actionable recommendations and insights to drive business strategies and improve performance.

Data Analyst Skills

While it’s ideal for making a list of prerequisites based on a company-specific data analyst job description, you must always be ready with the essential skills and qualifications in your armoire. You must combine technical expertise, analytical thinking, and strong communication skills to champion the competition as a data analyst.

Here are five essential skills and qualifications that power various data analyst responsibilities:

Data Analyst Job Description
Source: Columbia Engineering

Proficiency in Data Analysis Tools

When scavenging for a job, you must have caught the need for knowledge of various data analysis tools and technologies in a data analyst job description. This skill involves proficiency in programming languages like Python or R and SQL for database querying and manipulation. In addition, the role strongly focuses on data visualization tools like Tableau or Power BI for creating impactful visualizations and reports.

Strong Analytical and Problem-Solving Abilities

Strong critical thinking skills to identify relevant questions, design analytical approaches, and solve complex problems are other vital skills in every data analyst job description. A professional with this designation should be able to break down complex problems into smaller components, apply logical reasoning to analyze data, and propose practical solutions or recommendations based on the findings.

Knowledge Of Statistical Analysis And Data Modeling

A data analyst role walks hand-in-hand with statistics and possesses analytical skills to make sense of complex datasets. Knowledge of statistical methods, hypothesis testing, regression analysis, and data modeling is crucial for uncovering meaningful insights and drawing accurate conclusions from the data.

Familiarity With Data Visualization Tools

Data analysts communicate their findings through reports, presentations, and visualizations. They create clear and concise messages that convey complex information in a visually appealing and easy-to-understand manner. They may use tools like Tableau, Power BI, or Excel charts to create visualizations that help stakeholders grasp the insights effectively.

Excellent Communication And Presentation Skills

Whenever the insights are ready, it all boils down to effectively communicating them to technical and non-technical stakeholders, which is crucial for a data analyst. This skill in a data analyst job description may go unnoticed, but it is often a game changer! It includes presenting data visually through charts, graphs, and dashboards and creating clear and concise reports. Moreover, strong written and verbal communication skills are vital for conveying complex information comprehensibly.

Education and Experience Requirements

The roles and responsibilities mentioned in a data analyst job description all boil down to the factors like educational background and relevant experience. Here is all that you need to know about these requirements:

data analyst job description future
Source: Resonance

Degree In A Relevant Field

To become a data analyst, you must carry a bachelor’s degree in a field such as statistics, computer science, mathematics, or a related quantitative discipline. Some employers may also require a master’s degree in a relevant area.

Experience With Data Analysis In A Professional Setting

More often than not, your dream data analyst job requires you to have experience demonstrating practical data analytics skills with an entry-, mid-, or senior-level job. The more knowledge you carry, the higher your chances of getting a call from the recruiter.

Familiarity With Database Systems And Querying Languages

To become a data analyst, you must also have a strong knowledge of statistical concepts and techniques and statistical software such as R, Python, or SAS.

Moreover, employers require candidates to carry proficiency in SQL and programming languages like Python or R.

Skills in Demand for Data Analysts Job
Source: analyticsindiamag.com

Career Growth and Opportunities

Top companies hiring data analysts
Source: nycdatascience.com

Whether you have landed your first data analyst job or are still meditating, it is essential to consider the prospects of the designation and the growth it brings. Here is what happens after you start your career as a data analyst:

Advancement Opportunities Within Data Analysis Field

Majorly, data analysts further become data scientists. Data science requires individuals to have advanced statistics, machine learning, and programming skills, allowing them to take on more complex and strategic projects. With additional training and experience, you can progress toward this role.

Potential To Specialize In Specific Domains Or Industries

In the vast field of data analysis, you have a scope to specialize in various areas and make your career skyrocket. From financial data analysis to marketing analytics, there are many industry-specific domains to work in.

Continuous Learning And Professional Development

With new technologies, methodologies, and best practices emerging in data analysis, you can move beyond what you initially saw in a data analyst job description. To shape a winning trajectory, you must pursue advanced learning by participating in professional development opportunities, data science courses, and even joining data analytics communities.

Conclusion

So, that was all about the data analyst job description! Apart from skills and qualifications, it is imperative to gain relevant experience to get a job as a data analyst. However, when it comes to standing out, earning certifications in data science or appropriate fields is the best route. One highly recommended certification is the AI & ML BlackBelt Plus program by Analytics Vidhya. This all-encompassing program explores the world of data science using the practical approach and real-world examples. Thus, making it an ideal learning platform for those seeking to hone their skills.

Frequently Asked Questions

Q1. Is data analyst an IT job?

A. The data analyst role does not typically fall into the IT category. It entails applying statistical techniques, concepts, and practices to gain insights.

Q2. Is data analysis related to coding?

A. Yes, basic coding is one of the many skills required to become a data analyst.

Q3. How difficult is data analytics?

A. Data analytics isn’t necessarily a problematic practice. The learning curve may be subject to the time spent learning and adaptability.

Q4. Is data analytics a good career choice?

A. Data analytics professionals with the right skillset are in high demand in the current job market. While the job opportunities are immense in this field, the number of qualified candidates is limited. This is because data analysts can command a higher-than-usual pay scale and many perks at an experienced and entry-level level.

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Analytics Vidhya

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