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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

Start Freelancing in Data Science/Data Analytics!

S
Shraddha Last Updated : 24 Jun, 2021
5 min read

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

Introduction

“Data Science” and “Data Analytics” are the most booming fields today and a large number of the crowd is inclined towards learning the same. Starting a career in Data Science and working in this field may not be as easy as it seems in the courses that are available online. You need to have a firm hold on critical thinking and analysis and need to pay great attention to details while solving any problem. Along with solving assignments and exploring the field you might as well get paid for it! keeping in mind that you have the knowledge about this field. So how to start freelancing in Data Science? There are many ways for doing so and we will look at some of those, mostly what tasks can you do as a freelancer in Data Science.

When you say freelancing, Fiverr is a name that pops into your head. This is a very vast platform for a huge variety of domains including Data Science. It’s very simple to start with and can be very helpful if you want to get a hands-on experience rather than just watching videos. There are many other platforms like Upwork, Kaggle, etc. These gigs apply for all of them as well, except for kaggle, it works a bit differently. Kaggle has many competitions going on throughout the year and they have a winning prize for the top 3.

To Do

First things first, create a Fiverr account. Write a nice bio.

Next is Gigs. In Fiverr you can create various Gigs for your offerings. You have to give a relevant and precise title for your gig and set a price for anything you want starting with $5, but should be appropriate. As gigs are what represent you on Fiverr you should put some effort into making it appealing by adding images related to your gig. Say it is for data visualization, then add some dashboards that you created. Check the image below to get an idea.

Also, don’t forget to write a proper description of the gig and what will be provided. Now for beginners, it is ok to start a gig with $5 and as you start getting recognized and start getting more requests, you can raise the prices. Almost 50% of the gig on Fiverr are $5-10 and that is why it’s a popular freelancing platform. Another important parameter can be your response time. Make sure you don’t take too long to respond and out on the request. Create multiple gigs with specific offerings.

Also Fiverr allows you to add milestones and get paid for each milestone. That will be all about building a profile, now let’s get started with what gigs are on-demand and useful.

Freelancing in Data Science

source: screenshot

Gigs

 

1. Create an interactive Dashboard/Visualization using Tableau, Power bi, or Excel

Many people are looking for a systematic visualization for their data as they don’t know how to do it or have no time for it. You are the one to offer them a visualization. There are a couple of data visualization applications. You can learn how to use them properly and start with this gig. Usually, people have this gig for $5 but, as you go on and get ratings/comments that will speak for your work, raise the price if you want.

2. Create an interactive Dashboard/Visualization using Python, Dash, Flask, Shiny, Django

If you have a good knowledge of python or any of the mentioned frameworks, this is a gig for you. Many people need a more customized dashboard as Tableau has limited operations. These frameworks will help in adding customization to the dashboard. There is a fewer number of gigs with python, frameworks than that of Tableau and Power bi. Again many of them starting with $5.

3. Help in completing Data Science Assignments

Another popular gig is solving assignments for people. A lot of people get stuck with an assignment and need help in solving it. You can increase your skills along with helping people also, make some money.

4. Converting Excel Dashboard into Python, Dash, Flask, Shiny, Django apps

A lot of times people create excel dashboards but need to add more functionalities that are possible using python. If you know how to work with these frameworks for data visualization, the conversion would be an easy task. You just replicate the excel dashboard with the same data.

5. Data cleaning and Feature Engineering

Data cleaning can be an easy and sometimes boring task but is an important step when it comes to precision. Whereas Feature Engineering sure requires skills and understanding of the domain. Understanding how the columns are related to each other or are completely unrelated, choosing the right features, and increasing the accuracy. Price can vary according to the scope of the project, usually $5-10 for beginner tasks.

6. Creating Machine Learning Models

Having a very good knowledge about ML algorithms and being aware of which model is suitable for the task is the requirement. If you are confident you can do it, then this gig can be very helpful in gaining practical knowledge. Starting simple models at $5 and can get higher for more complex models, even multiple models for multiple purposes.

7. Creating Deep Learning Models

Getting one step ahead, creating deep learning models. It may be a help in someone’s personal project as well, but people are ready to pay you if you can associate them correctly in completing it. Depending on your skills around $5-10 for initial assignments.

8. Web Scraping

There are also requirements like collecting data from a website for example after every 2 hrs or every day. Being familiar with APIs and web scraping you can write code for the same. Many offerings start with $5-10.

9. Teaching Machine Learning, Python, Web scraping, creating an interactive dashboard using the application of programming

Last but not the list, if you have detailed knowledge about any of the above-mentioned you are good to start teaching people on an hourly basis for 2-7 days. You may also keep a special 2hr session gig to explain any of the topics related to the domain that the customer wants to learn instead of the whole course. Provide proper instruction in the description about the platform that will be used for the lessons. Try to keep multiple options like zoom, google meet, skype, etc.

Challenges

Every good thing comes with a price and the price here is efficiency and honest work. Being a great platform Fiverr has hundreds of other candidates offering the same service as yours, and there are few things you need to take care of to stand out, get more offers and sustain among the huge crowd.

1. Response time

You don’t wanna miss out on a request just because you were late to reply. After receiving an email notification, try to reply asap.

2. Communication

Getting to know all the correct needs of the customer is very important and this should be done at the very beginning. Once you have completely understood it, you’re good to go. Also, keep communicating throughout even if you face any problems. Communication also helps in negotiations so don’t let that slip.

3. On-time delivery

You have to set a deadline for your gig and it’s a good practice to follow it. Delivery before the deadline is even better. But failing to meet the deadline and taking way too much time can sometimes lead the customer to cancel the order. Yes, it is possible to cancel the order or reject it if it’s not satisfactory. On the other hand, delivering an excellent project can get you tips as well.

4. Good work

Never accept something that you don’t completely know. You have a deadline and you have to submit a neat project in order to sustain and to get more orders in the future. As a freelancer, this is an important factor.

So, these are some gigs you can get started with side by side and earn some experience and money. There are tons of other gigs as a programmer like doing python work, teaching any programming language, helping with android apps, JavaScript, etc, but these are the ones for data science and data analytics. Hope this helps in starting your journey in freelancing in Data science.

Author

Shraddha Shekhar

Computer Engineering student, exploring the field of Data Science and Deep Learning.

LinkedIn: Shraddha Shekhar

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.

S
Shraddha

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Responses From Readers

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Varuna Chauhan
Varuna Chauhan

Really nice and informative blog.

123

Immersive Animator
Immersive Animator

Really helpful and worthwhile content, Thank you.

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Jonathon
Jonathon

Thanks for sharing some useful info about the data sciences as freelancer I am a data science analytics for around 2 years but never knew that this may be continued as freelancer. Thanks mate!

123

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