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Reading list

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
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 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

Future of Data Analyst

ayushi9821704
ayushi9821704 08 Aug, 2024
7 min read

Introduction

Envision a reality where data is not just an array of figures but a tool that serves as the blueprint for all management decisions. In this ever-changing environment, the data analyst becomes crucial. They convert raw data into usable analysis. This analysis can be used to optimize organizational strategy and processes. As technology progresses and data becomes more sophisticated, the role of data analysts will change significantly. Their job will evolve to meet new demands and opportunities.

This conceptual article discusses these changes to understand new trends, skills, and potential careers for data analyst.

Future of Data Analyst

Overview

  • You will learn the growing trends pertaining the field of data analytics.
  • Determine which new competencies and tools are pertinent to the data analyst profession.
  • Discuss possibilities of work in the field and the ways to advance in data analytics.
  • Take into account definition and relevance of analysis in fields involving artificial intelligence and machine learning.

Table of contents

The role of data as a source of information is rapidly changing. New trends in data management influence the processes of its collection, analysis, and use. New technologies and methodologies increasingly drive improvements in data analytical techniques. These advancements enhance the ability to find useful information in large data sets. It is crucial for data professionals to stay updated on these trends. This helps them realize the full value of data assets.

Rise of Big Data

This element is characterized by a high growth rate due to the increasing number of such sources as social media, IoT devices, and digital transactions. Working data analysts will have to deal with large data volumes, which may call for the use of rather complex tools and methods to process big data.

Integration of Artificial Intelligence and Machine Learning

AI and ML are transforming the data analysis by automating it, improving most methods that use predictive analysis, and finding features which naked eyes may not identify. Analysts will have to incorporate these technologies in their work to enhance their data analysis tools, thus arrive at more enhanced and elaborate projections.

Generative AI and Large Language Models

The advent of Generative AI and LLMs such as GPT-4 is transforming how data is analyzed and interpreted. These technologies can generate human-like text, summarize large datasets, and even create predictive models, allowing data analysts to focus more on strategic decision-making rather than manual data processing. By training on vast amounts of text data, LLMs can provide insights, detect anomalies, and suggest optimizations, making them invaluable tools for data analysis.

Generative AI

You can also enhance your career as a data analyst by enrolling in our GenAI Blackbelt Course. This comprehensive program is designed to equip you with the advanced skills and knowledge needed to excel in the evolving field of data analytics.

Real-Time Data Analytics

With the rise of connected devices and smart technology, there is an increasing need for real-time data analysis. Analysts will need to develop skills in technologies that support real-time data processing to provide timely insights and make swift, data-driven decisions.

Increased Emphasis on Data Privacy and Ethics

Data collection is becoming more widespread and with it, issues of privacy and the appropriate handling of data also arise. The professionals in data analysis will have to make sure that data is processed and managed legally as well as following the guidelines of GDPR or CCPA.

Evolving Skills and Technologies

Data analytics requires constant adaptation to constantly changing skills and technologies. This model encourages the multiplication of tools and methodologies, influencing data professionals’ activities. This includes developing new analytical methods and using advanced software. Companies must accept these changes to remain competitive and adapt to the current data environment conditions.

Advanced Statistical Analysis

The next generation data analysts will be required to have more sophisticated skills in statistical techniques like, Multivariate analysis, Bayes theorem and Hypothesis testing to name but a few. All these will assist in managing massive information and infer additional pertinent information.

Programming Skills

Adequate knowledge of such programming languages as Python and R will be vital. Most data scientists use these languages for data manipulation statistical modeling and training of machine learning algorithms. It is desirable that analysts are comfortable with creating programs and detecting the error in the written lines to analyze the data.

Data Visualization

Creating creative and engaging data visualizations is crucial for conveying insights. Popular BI tools like Tableau, Power BI, and D3.js are essential for designing visually appealing designs that effectively communicate results to consumers.

AI and ML Knowledge

Understanding AI and ML algorithms, how the model is built from data, and how those models are integrated into processes will become more necessary. These applications will require data analysts to team with data scientists and engineers in order to incorporate them into organization’s high level analysis and use of automation.

Knowledge of Large Language Models (LLMs)

LLMs such as GPT-4 can automate the extraction of insights from unstructured data, generate natural language summaries, and even perform sentiment analysis. Data analysts need to understand how to leverage these models, integrate them into their workflows, and interpret their outputs effectively.

Soft Skills

It is in communication and the ability to tell a compelling story that difficult data findings are turned into sound business solutions. Data analysts should design interactive dashboards to present information clearly and communicate it easily to others in the organization.

Career Paths and Opportunities

Currently, the field of data analytics offers a wide range of jobs and professions. These roles cater to different interests and strengths. The variety of roles, from complex statistical calculations to data engineering and business strategy, ensures opportunities for professionals. They can find niches that match their abilities and personal interests. By analyzing these orientations, a person can decide on the best path for growth. This helps them make informed decisions about their career trajectory.

Specialization

Data analysts can choose to work in various areas, such as financial analysis, healthcare analysis, marketing analysis, and supply chain analysis. Specializing in a particular industry allows analysts to gain in-depth experience. This expertise enables them to provide reports that are valuable for making strategic business decisions in that industry.

Leadership Roles

With the importance of data continuing to rise in organizations, the development of organizational strategies around data will increase. This will lead to more vacancies in higher-level executive roles, such as CDO or Director of Data Analytics. These roles require one to coordinate the data initiative and supervise a group. They also ensure that the change resulting from the data initiative is company-wide.

Consulting and Entrepreneurship

As is true with many professions, skilled professionals may opt to venture into the lucrative business of providing consultation. They may also choose to own companies that focus on data analysis and related services. This path enables them to work with different clients. They can offer the right strategic advice and solutions for various projects. This approach allows them to cater their skills to a broader audience.

Impact of AI and ML

AI and ML are revolutionizing data analytics to an extent that one can term these technologies as the new norm. These technologies allow for a better examination of the circumstances, identify hidden motives/positions, and perform analyses that were previously monotonous, and labor-intensive. Incorporation of AI and ML in the analytics tools and processes strengthens the predictability, innovation, and opportunities for data professionals. It is essential to comprehend them in order to maintain competitiveness in the growing sphere of data analysis.

Automation of Routine Tasks

AI and ML can enable the automation of some processes, such as data cleaning and preprocessing. This automation relieves data analysts from data gathering and cleaning tasks. As a result, analysts can spend more time handling analytical tasks. They can also focus on decision-making at a strategic level.

Enhanced Predictive Capabilities

According to the above mentioned points, the use of machine learning algorithms is useful to enhance the predictive models which can provide the accurate forecasts. Data analysts must alter these models to fit business planning and make accurate predictions.

Generative AI for Data Analysis

Generative AI models can produce new data points, simulate various scenarios, and predict potential outcomes. These models can be used to create synthetic data for training, testing, and validating analytical models, thereby enhancing the robustness and accuracy of predictions.

Integration with Business Processes

AI and ML will be more and more a natural part of business processes so that data driven decision making becomes natural. Experts must familiarize themselves with how to use these technologies to improve task delivery and achieve organizational goals.

Conclusion

The future of data analysts is on the brink of drastic change due to several factors. New trends and career advancements will shape their roles. By exploring these options, data analysts can actively create the future of business and organizational landscapes. Keeping up with such changes will help make sure that data analysts retain their relevance in the current and future methodologies.

You can also enhance your career as a data analyst by enrolling in our GenAI Blackbelt Course. This comprehensive program is designed to equip you with the advanced skills and knowledge needed to excel in the evolving field of data analytics.

Frequently Asked Questions

Q1. What are the emerging trends in data analytics?

A. Key trends include big data growth, AI and ML integration, real-time data analytics, and increased emphasis on data privacy and ethics.

Q2. What skills will data analysts need in the future?

A. Data analysts will need advanced statistical analysis skills, programming proficiency, data visualization expertise, AI and ML knowledge, and strong communication skills.

Q3. What career opportunities are available for data analysts?

A. Opportunities include specialization in various domains, leadership roles, consulting, and entrepreneurship.

Q4. How will AI and ML impact the role of data analysts?

A. AI and ML will automate routine tasks, enhance predictive capabilities, and integrate into business processes, reshaping the role of data analysts.

Q5. How can data analysts prepare for future changes in the field?

A. Data analysts can prepare by staying updated with technological advancements, developing new skills, and seeking opportunities for specialization and leadership.

data analysisdata analyticsFuture of data analyst
ayushi9821704
ayushi9821704 08 Aug, 2024

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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