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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

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

Mrinal Singh
Mrinal Singh Last Updated : 15 Jun, 2021
5 min read
This article was published as a part of the Data Science Blogathon
Data Science Is A Highly Advanced Field Because It Varies From Industry To Industry

Photo By Marc Rafanell López from Unsplash

Numerous personalities relish for granted how much of modern society is made possible by the hard work of highly dedicated and highly educated scientific and technical specialists.

After all, for every fantastic advancement in communication or transportation, countless brilliant professionals are working tirelessly to maintain that advancement, not to mention those innovators who actually developed it.

Furthermore, things have gotten so complicated that even highly technical organizations and companies have to hire outside consultants for highly complex aspects of maintaining their work.

One example of this is data science, an advanced and highly contemporary field involving analyzing and using complicated databases.

Data Science Is A Highly Advanced Field Because It Varies From Industry To Industry

There are countless different industries for which data science can be an exceptional service, and the specifics of how it is applied varies from industry to industry. But, of course, there are some basic commonalities, as the principal behind data science and data analysis remains the same, regardless.

The main goal of investing in data science is to transform massive and excessively complex databases into learning that can be accepted for reasonable objectives.

To do this, you cannot simply sort through all of the data manually, as it would take considerably extraordinarily long and would not be meriting the time. That is why data scientists spend a lot of their time developing algorithms to sort through this data automatically.

Then, different algorithms are used to discern information from the sorted data. At this point in the data analysis process, you have helpful information determined through statistical analysis.

Furthermore, they can even use this information to determine probabilities for various outcomes. This can even improve safety features in some industries, such as engineering, medicine, and air traffic.

In addition to reducing unsafe practices and features, it can also be used to isolate inefficiencies to save your company large sums of money, in some cases millions of dollars.

This is one of the biggest reasons why so many large companies have begun to invest in data analysis consulting in recent years. It is a way to make large systems more functional, and even a tiny increase in efficiency has far-reaching positive consequences on a large scale.

Sometimes Data Is Difficult To Handle

 

Sometimes Data Is Difficult To Handle
Photo By Nik Shuliahin from Unsplash

There are so many different fields of science that it can be tricky to keep up sometimes! From medical science to computer science, the possibilities are endless! There is one kind of science. However, that is particularly helpful when you have a data management problem on your hands. Years ago, people would never have imagined that there would ever be a problem with too much data to process efficiently or effectively. Still, here we are today in the information age with too much information and too little time.

What is there to do?

Luckily, there is a conducive field when it comes to clearing up those data management woes! You can fairly quickly develop a system to process data when you utilize the data science to your advantage and have plans on hand to understand the many layers of data you are faced with.

Some people are particularly good at this work, and many data analytics consulting firms are full of just those people! If you are running into a data analysis conundrum, find a firm to help you make a dent in that data and come up with a plan for processing the information that can seem so very daunting!

Data Mining Can Solve Your Raw Data Problem

An aspect of this field of computer science is known as data mining. Data mining is accurately what it reflects. It is like mining gold from a mountainside, except with a lot less physical labor. Utilizing systems and aspects of computer science can be easier to locate just the kind of data you are looking for when swimming through a sea of data points.

The key to this kind of work is finding a model through which the data can be understood and learning how to replicate that system for future use.

Extracting the data is one thing, but being able to continue using that information in powerful and productive ways is just as important. The process through which this data is worked through and understood relies on a few key steps.

  • First, there is the raw analysis of the information, of course.
  • Then there are the database and data management aspects, which allow you to use the data that is available in such a way as to allow for deeper understanding.
  • There are also several different metrics through which the data can be processed and understood.
  • For example, a metric of interestingness can help determine the necessary information.

Incidents where selecting a data science consulting agency is a more suitable alternative.

Data science projects can be controlled by choosing a consulting firm. Data science consulting is extra beneficial than other ways:

  • There is no proper off-the-shelf answer for your problem statement
  • The funds are not sufficient to make an in-house team.
  • Data science projects don’t demand unique fashionable data
  • The data set does not comprise delicate data
  • Your company requires the administration to identify the market characters of data science plans.

4 Factors to Examine When Picking Data Science Consultant

These 4 rules can help determine the accurate data consulting partner:

  • Educational degree of consultants
  • Involvement In Solving Real-World Projects
  • Term of service they give
  • Analytics expertise

 

4 Factors to Examine When Picking Data Science Consultant

Can You Consult A Data Science Consulting Firm?

Though the main essential aspects of data science and data analysis have to do with statistics and probability and developing complicated algorithms, there are other, industry-specific bits of knowledge that data science consultants must have. For this reason, for data science to actually be helpful and applicable, the people consulting for you have to know your industry.

For this purpose, if you can discover a data science consulting firm that is reputable and applicable to your industry, you should consider consulting with them. No matter how technical your field, there is undoubtedly an analytics consulting firm with professionals who can apply their data science knowledge to the specific needs of your company, organization, or other institution.

 

Best 3 Data Science Consulting Firms In India

The best consulting firms for Data Science assistance are:

BRIDGEi2i Analytics Solutions:

A trusted co-worker for facilitating AI for Digital Enterprises by leveraging data engineering, high-level analytics, established AI accelerators, and Consulting expertise, BRIDGEi2i dominates over adding content to customers and resolving complicated business difficulties by expanding contextual solutions.

Course5 Intelligence:

Course5 Data Science organization (DSO) is concentrated and targeted on crucial industries that follow organizational policies and focusing on resolutions targeted businesses, marketing, customer, stock, and supply chain.

Genpact:

Genpact uses data and domain-led analytics to alleviate uncertainty, raise boundaries, and promote growth. By providing opportunities from big data, machine learning, data engineering, and data science, Genpact produces actual business issues. Genpact is one of the engineers in installing advanced analytics into company rules.

 

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. Mrinal is also a freelance blogger, author, and geek with over 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

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kunal gaikwad

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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
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
What data mining can do for your company and Practical Uses of Data Mining in Businesses
What data mining can do for your company and Practical Uses of Data Mining in Businesses

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