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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

A Quick Guide to Become a Data Analyst

krrai77@gmail.com
[email protected] Last Updated : 11 Dec, 2020
5 min read

This article was published as a part of the Data Science Blogathon.

Explore the simple path to become a great visual data storyteller

Are you someone who loves data, but not keen on becoming a data scientist? Do you enjoy finding a needle in a haystack? Are you a creative thinker who is always curious? Then the role of a data analyst/data visualizer/data storyteller might be apt for you.

Not everyone is into coding, mathematics, and stats. However, the passion of working with data, exploring the hidden insights in the data, and presenting these insights in a simplistic manner might be something that you want to dabble in. This article will walk you through the skills, tools, and resources required to become a data analyst.

 

Technical skills

The role of a data analyst does not demand a computer science or math background. You can acquire the technical skills required for this role even if you are from a non-technical background. Following is a list of key technical skills required to ace the data analyst role:

  • Programming: The level of coding expertise required for a data analyst is not as high as that of a data scientist. You need to have the ability to explore and analyze huge datasets. This is achieved using data visualization tools such as Power BI and Tableau. However, not all companies can afford to purchase these tools. Therefore, the ubiquitous choice is to use Python and its extensive data visualization libraries.Python is the best choice for anyone new to coding. It’s very easy to learn and the most widely used programming language in data science. You can manage with basic Python skills and master the key libraries required for this role that includes Pandas, Matplotlib, Seaborn, Numpy, and Scikit Learn.
data analyst career - programming

Basic knowledge of SQL is also helpful as it will give you control over accessing the data from various sources. Understanding data retrieval and integration will help in managing the data well.

  • Tools: Data visualization tools are what drive most of the data analyst roles. Mastering these tools does not require a technical background. Most of these tools work on a click and drag basis. The important aspect is to understand various functionalities required to analyze and visualize data using these tools. According to Gartner’s 2020 Magic Quadrant, the top business intelligence and analytics tools in the market are Power BI and Tableau.You can learn both of them or focus on one and gain mastery. Both the tools have free cloud versions available that has most of the features required for data analysis. Basic knowledge of using these tools is accomplished within a few weeks with the help of free tutorials available online. You will need some effort to gain expertise in the advanced level of using these tools that includes various calculations, formulas, developing custom visualizations, and slicing/dicing data. It is essential to master at least one of these tools thoroughly.
Data Analyst Career - Tools

Excel is another widely used data analysis tool but underrated for its capability. Its features are on par with that of Power BI as Microsoft has brought in most of the Power BI tools such as Power Query editor to Excel. There are many features in Excel such as pivot tables, formulas for data manipulation, and charts for visualization that is effectively used to develop some amazing dashboards.

  • Business/Domain Knowledge: Domain knowledge implies understanding the customers’ business environments, competitors, and the business‘ overall foreseeable future. Every data analyst must spend sufficient time on acquiring the business/domain knowledge related to the problem statement. This will equip you with the skill to understand the problem from different perspectives and come up with the best possible solution.

Soft skills

Data analyst require more proficiency in soft skills compared to a data scientist. The onus will be on a data analyst to effectively present the outcome of data analysis to a panel that might include significant stakeholders such as manager, customer, and SME (Subject Matter Expert). You need to have the essential soft skills to churn the technical information into easy to understand non-technical action item. You have to confidently present your findings and put forth solutions through an impressive presentation. Here are some of the key focus areas required for this role:

  • Storytelling: Your dashboard consisting of all the charts and data is not enough if it’s simply colorful and nice to view. Each item in the dashboard should convey a message and together you have to weave a problem combined with a solution-based story to the stakeholder. In the given timeframe, you must have the ability to convey a story of your findings without confusing the user. Your story should be concise, simple, and precisely highlight the problem area.
  • Presentation: “A picture is worth a thousand words”, is a popular saying emphasizing the importance of visuals. A visual analyst knows exactly the type of charts to use for various data comparison/analysis. The dashboard used for presentation should not be a chaos of figures and visuals that is difficult to comprehend for the end-user. Your presentation must direct the attention of the stakeholder to key focus areas in the data.
soft skills
  • Communication: Having good communication skills is very important as communicating how insightful the results are, and how it can help customers in improving the profits is crucial. You need to look at the problem from different perspectives. Structured thinking is key to this. Continuously asking questions is one of the most important aspects of thriving in this role. A data analyst needs to be curious and always learning.

 

Certifications

There are many certifications available for validating your skills in this role. However, I would recommend opting for those given by industry leaders such as Tableau (Salesforce) and Microsoft. I am also including two important certifications related to data analytics from IIBA and TDWI. Following are the key certifications that you can check out:

 

Job Prospects

U.S. Bureau of Labor Statistics reported that the demand for data science skills will drive a 27.9% rise in employment through 2026. Demand for skilled data analytics professionals currently outweighs the supply.

As already discussed, having only technical knowledge is not enough for this role. Analytical thinking and the ability to picture numbers into patterns is very critical for this role. Alongside, the business analyst role is merging with that of a data analyst, and business analysts are one of the most in-demand professionals. So leveraging this opportunity, it’s the right time for people wishing to transition to this field from marketing/sales/business profession.

Whether you are fresh out of college or a veteran in the industry, the prospects of starting your career as a data analyst, or transitioning to this role are very much possible with the right balance of technical and soft skills. Make use of online platforms such as Tableau gallery to showcase your skills. Tableau has a big online community that is very active.

 

Conclusion

These were some of the key points that I wanted to discuss becoming a successful data analyst. Focus on your shortcomings and master those skills, as this is a very competitive field. Develop interesting dashboards using Tableau/Power BI/Excel and share it on LinkedIn to be noticed, and reach potential employers.

PS: I have provided all the necessary resources as hyperlinks throughout my article. 

krrai77@gmail.com
[email protected]

I am a tech-savvy data analyst and a passionate tech blogger. With an insatiable curiosity for the latest in technology and a knack for turning raw data into meaningful insights, I'm on a constant quest to explore the ever-evolving digital landscape.

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Thank you for sharing. It is a really good article. Can you please update the article? It would be really helpful. Thanks.

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