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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

The Right Data Science Consultation Help Can Enable You To Actually Make Use of Your Data and The Need For Data Science To Be Dynamic

Mrinal Singh
Mrinal Singh Last Updated : 11 Jun, 2021
4 min read

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

There are a lot of ways in which major companies and organizations utilize data compilation. Of course, being able to compile a massive amount of data does you no good whatsoever in and of itself. That being said, with the proper data science consultant help, you will be able to make use of your data in a way that will benefit your company or institution. It is well worth your investment as long as you deal with adept and knowledgeable data scientists. They have much experience dealing with an array of different highly technical and highly prominent industries. They have provided their services to countless highly successful companies and other institutions, including government organizations.

 

Data Science Consultation

Photo By @sigmund From Unsplash

Why Hire Data Science Experts For Your Company?

Data science is one of the most critical fields today, as it can have such profound effects on how things are done on such a massive scale. After all, data science is a field that mostly has to do with helping huge organizations coordinate highly complex networks and processes.

With such high stakes, it is easy to see why it is crucial to hire only highly reputable data science consultants. Their level of competence will have a direct and significant effect on your company and how it operates.

Data Science Is Not Easy

The nature of data science is complex. This is true, regardless of the industry you are involved in, though the specifics of how it will affect you vary accordingly.

Even those with a firm understanding of probability and statistics often struggle with some aspects because it requires computer science knowledge and specific knowledge regarding the industry for which analytics consulting is being done.

So, it is not hard to see why only highly skilled experts can legitimately call themselves data scientists.

Domains Which Can Benefit From Right Data Science Consultations

There is a long list of industries that can benefit from data science consultation. Among the industries that utilize it the most are the manufacturing industry, the medical industry, the banking industry, and air traffic.

Air traffic is an important concept, and it is relevant to various companies and institutions. The government has many different organizations, which require air traffic logistical help, precisely what data science can provide.

The same is true for commercial airlines, though, and delivery services, which utilize aircraft. Data analysis is critical when it comes to air traffic because it is challenging to coordinate all of the planes operating throughout the world.

The only way this is possible is if you have a very firm understanding of the patterns of all of your flights. Of course, this is only possible if you have help from data mining experts, who can turn your unintelligible mass of data into useful information.

The Need For Data Science To Be Dynamic

 

Data Science Consultation dynamicism

Photo By @jan_huber from Unsplash

Data science, even today, continues to confuse some people on what it is. Defined, it is the extraction of practical knowledge from data. The term “data science” was used by Peter Naur as an alternative to the term computer science. Naur is best known for his contribution to the development of the programming language ALGOL 60.

The confusion likely arose when statistician C.F. Jeff Wu presented a lecture wherein he posited using data science to mean statistics and renamed statisticians to data scientists.

Statistics In Data Science

Statistics is a field of study that mainly deals in collecting and organizing data up to the analysis and interpretation of the data, including the presentation. Thus, statistics look into the different aspects of data, including designing experiments and surveys. Considering that data science includes data mining, then it is possible that statistics cannot simply be applied in such a case.

This is where the difference between statistics and data science comes in. A statistician needs to become an expert in the field before he or she can practice. On the other hand, a data scientist can be an expert in one or two disciplines and proficient in another set of domains.

A statistician needs to be an expert in statistics. A data scientist can be an expert in statistics and be skilled in computer science. However, since it is unlikely that a person can develop such expertise early in their lifetime, data science consulting is typically done as a team. Hence, a team where each member has either the proficiency or expertise in the disciplines related to computer science, mathematics, or statistics.

Data Science Has Been Active In Two Main Areas Of Concern: Security And Clinical Data

In terms of safety, data scientists have long been involved in security and monitoring fraud. Through the use of large streams of data, scientists have discovered various threats and can prevent forgery.

In terms of clinical data, scientists have been able to help by providing the needed insight given a large amount of data, thus allowing pharmaceutical companies to know how safe and effective their new drugs are.

Whereas data scientists used to be simply consultants, now in the pharmaceutical field, they have more strategic roles by being part of the planning up to communicating the result of the clinical trials.

Conclusion

Indeed, the field has come a very long way from simply being an alternative term to computer science or statistics to being an actual profession. With information being passed faster and more efficiently, data scientists will need to keep up with this growing trend by increasing their competencies and skillsets. This discipline will most likely continue to evolve and could include new domains in its fold. With new emerging fields continuing to come out under this fascinating discipline, data science will be the thing to watch for in the years to come.

About Author

Mrinal Walia is a professional Python Developer with a Bachelors’s degree in computer science specializing in Machine Learning, Artificial Intelligence, and Computer Vision. In addition to this, Mrinal is a technical blogger, author, and geek with four years of experience in his work. With a background working through most areas of computer science, Mrinal currently works as a Testing and Automation Engineer at Versa Networks, India. My aim to reach my creative goals one step at a time, and I believe in doing everything with a smile.

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

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Mrinal Singh
Mrinal Singh

Data Scientist and a Technical Writer! I will give you the best of Open-Source and AI.

Talks about #chatgpt, #opensource, #contentcreation, #communitybuilding, and #artificialintelligence

Technical Writer | Data Science, ML, AI, Open-Source | Do More with Data - Litmus

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Why Is Data Science Still Considered A Contemporary and Highly Advanced Field? And Can You Consult A Data Science Consulting Firm?
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