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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

Top 5 Reasons to Become a Data Scientist

Ayushi Trivedi
Ayushi Trivedi Last Updated : 05 Feb, 2024
6 min read

Ever thought about how all the information swirling around us is like a hidden power shaping the way businesses and the world work today? In this digital age, data is like the magic behind the scenes, connecting everything and everyone. But here’s the catch – there’s so much data that we need smart people to make sense of it all. Enter data scientists, the superheroes of the digital world! So, have you ever wondered why more and more folks are getting excited about becoming data scientists? Let’s dive into the top five reasons why jumping into the world of data science is like unlocking a door to a cool and rewarding career.

Data Scientist

What is a Data Scientist?

Before delving into the reasons for choosing a career as a data scientist, it’s essential to understand the multifaceted nature of this profession. Imagine blending elements of a mathematician, a computer scientist, and a business strategist into a single role – that’s the dynamic world of a data scientist. Navigating through diverse disciplines, these professionals must seamlessly integrate expertise in mathematics, computer science, and business strategy.

The intricate skill set required for this role demands that data scientists maintain a dual presence. They need to be deeply rooted in the ever-evolving landscape of information technology while simultaneously having a solid foothold in the dynamic realm of business. This intricate balance is a key factor driving the high demand for data science expertise and underscores why opting for a career as a data scientist stands out as a strategic and rewarding choice.

A data scientist is a professional who analyzes and interprets complex data sets to inform business decision-making. The role of a data scientist involves a combination of skills from various fields, such as statistics, mathematics, computer science, and domain-specific knowledge.

Here are some key aspects of the data scientist role:

  1. Data Analysis: Data scientists are proficient in using statistical techniques and programming languages to analyze large volumes of data. They explore data patterns, trends, and relationships to extract meaningful insights.
  2. Machine Learning: Many data scientists apply machine learning algorithms to build predictive models and make data-driven predictions or classifications. This involves tasks like regression analysis, clustering, and classification.
  3. Programming: Proficiency in programming languages like Python or R is essential for a data scientist. They use these languages to manipulate and process data, as well as implement machine learning algorithms.
  4. Data Cleaning and Preprocessing: Much of a data scientist’s time is spent on cleaning and preprocessing data to ensure its quality and relevance. This includes handling missing values and outliers and transforming data into a suitable format for analysis.
  5. Data Visualization: Data scientists use visualization tools to present their findings in a clear and understandable manner. Visualizations such as charts, graphs, and dashboards help communicate complex insights to non-technical stakeholders.
  6. Domain Knowledge: Depending on the industry or sector they work in, data scientists need to have a good understanding of the specific domain. This allows them to interpret data in a contextually relevant way and derive actionable insights.
  7. Communication Skills: Data scientists need to communicate their findings effectively to both technical and non-technical audiences. This involves translating complex technical details into insights that can be easily understood by decision-makers.
  8. Problem Solving: Data scientists are problem solvers. They identify business challenges or questions, formulate hypotheses, and use data to test and validate these hypotheses. Their goal is to provide solutions or recommendations based on their analyses.

Data scientists help organizations leverage their data to gain a competitive advantage, make informed decisions, and optimize business processes. The field is dynamic, with ongoing advancements in technology and methodologies, making it essential for them to stay updated on the latest developments in their field.

Data Science

For those seeking a deeper understanding of the data science profession, you can also read here. If you are still in the early stages of exploring the possibility of a career or educational path in data science, we recommend evaluating the following top five reasons as compelling motivations to enter this field.

Reasons

1. High Demand and Lucrative Salaries:

The digital revolution has ushered in an era where data is more valuable than ever before. Organizations are increasingly recognizing the importance of leveraging data to make informed decisions, drive innovation, and gain a competitive edge. Consequently, the demand for skilled data scientists has soared. According to various industry reports, there is a substantial shortage of qualified data scientists, making it a candidate-driven market.

The scarcity of talent in this field has led to lucrative compensation packages for data scientists. Professionals with expertise in data science, machine learning, and artificial intelligence often command high salaries, reflecting the critical role they play in helping organizations harness the power of data. Aspiring individuals can capitalize on this demand-supply gap and embark on a career path that not only offers financial stability but also promises continuous growth and development.

The salary for data scientists can vary based on factors such as experience, location, industry, and company size. Here is a salary bracket of how much a they can make in a year.

Data Scientist₹12-14L/yr
Senior Data Scientist₹22-25L/yr
Lead Data Scientist₹26-29L/yr

Keep in mind that these figures are approximate, and the actual salaries can change. Additionally, salary information is often subject to fluctuations in the job market. For a deeper understanding of salary insights in the dynamic field of data science, consider exploring data from platforms such as Glassdoor.

2. Versatility and Cross-Industry Applicability:

One of the most appealing aspects of a career in data science is its versatility and cross-industry applicability. Data scientists are not confined to a specific sector; rather, their skills are in demand across various. This versatility provides professionals with the opportunity to explore different domains and apply their expertise to solve diverse challenges.

For instance, a data scientist working in healthcare may analyze patient data to enhance treatment outcomes, while someone in finance may use data science techniques to identify market trends and optimize investment strategies. This cross-disciplinary nature of data science not only keeps the work interesting but also allows individuals to make a meaningful impact in areas they are passionate about.

3. Impactful Decision-Making and Problem Solving:

Data scientists are instrumental in transforming raw data into actionable insights, enabling organizations to make informed decisions and solve complex problems. The ability to extract valuable information from data empowers businesses to streamline operations, optimize processes, and identify opportunities for innovation. This impact on decision-making and problem-solving makes them indispensable contributors to an organization’s success.
Imagine leading a company’s strategic decisions, predicting market trends, and enhancing efficiency through data. Data scientists drive innovation, shaping businesses for long-term sustainability.

4. Continuous Learning and Technological Advancements:

The field of data science is dynamic and ever-evolving, driven by rapid technological advancements. This constant evolution presents with the exciting prospect of continuous learning. From staying updated on the latest algorithms and tools to mastering emerging technologies like artificial intelligence and deep learning, the journey of a data scientist is marked by intellectual curiosity and a commitment to ongoing education.

For individuals who thrive on intellectual challenges and enjoy staying at the cutting edge of technology, data science provides an ideal playground. The continuous learning curve not only keeps the profession engaging but also ensures that they remain relevant and in-demand in a rapidly changing technological landscape.

5. Contribution to Societal Progress:

Beyond the corporate realm, data scientists have the opportunity to contribute to societal progress and make a positive impact on the world. The application of data science in fields like healthcare, climate science, and social justice allows professionals to address some of the most pressing issues facing humanity.

For example, data scientists working in healthcare can analyze medical data to identify patterns that lead to better diagnoses and treatment plans. In climate science also they play a crucial role in analyzing environmental data to understand and combat climate change. By leveraging their skills for social good, data scientists have the potential to contribute for positive changes on a global scale.

Conclusion

Becoming a data scientist offers a unique and exciting career path with a multitude of compelling reasons to pursue it. From the high demand and lucrative salaries to the versatility and cross-industry applicability of the skills, data science provides a wealth of opportunities for individuals seeking a dynamic profession. The continuous learning curve and the chance to contribute to societal progress further add to the allure of this burgeoning field. Aspiring data scientists not only embark on a journey of professional growth but also position themselves at the forefront of innovation, where their analytical skills can shape the future of industries and contribute to the betterment of society as a whole.

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AIdata scientist
Ayushi Trivedi
Ayushi Trivedi

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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