Getting Started with Data Science
Step into the world of data science now and open the doors to opportunity.
Crack Your Next Interview
Want to make it through the next interview you will appear for? Hone your skills with our five-part series of interview questions widely asked in the industry. With basic to advanced questions, this is a great way to expand your repertoire and boost your confidence.
Wipe the slate clean and learn Data Science from scratch
Data Science vs Data Analytics
Data Science Skills to Boost your Career
Future of Data Science
Top 10 Data Science Books
Skills to Become a Data Scientist
Best Data Scientist Certifications
Data Scientist Salary in India
Data Science Interview Questions- Part 1
Data Science Interview Questions- Part 2
Data Science Interview Questions- Part 3
Data Science Interview Questions- Part 4
Data Science Interview Questions- Part 5
Get started using Python
Python Data Science Introduction
Python Data Science Environment Setup
Python Matplotlib
Data Operations and Data Cleansing
Processing CSV, JSON, and XLS data
Python Relational Databases
Python NoSQL Databases
Stemming and Lemmatization
Data Wrangling and Aggregation
Box Plot and Scatter Plot with Python
Bubble Chart and 3D Charts in Python
Geographical and Graph Data in Python
Python Time Series Evaluation
Measuring Central Tendency and Variance
Normal, Binomial, Poisson, Bernoulli Distributions
p-value and Correlation
Chi-Square Test and Linear Regression
Heat Maps with Python
Histograms and Bar Plots with Python
Discover Big Data
What is Big Data?
Big Data History, Technologies, Use cases
Apache Flink- Big Data Processing Framework
Big Data Use Cases- Hadoop, Spark, Flink Case Studies
Careers and Job Roles in Big Data
Interview Experience- How An Individual Cracked 11 Big Data Interviews
Big Data Terminologies and Hadoop Concepts
Vulnerability- the 10th V of Big Data
Lambda Architecture for Big Data
Big Data And Cloud Computing
10 Best Big Data Analytics Tools
Spark Hadoop Cloudera Certifications
Big Data Application- Income Tax Department
How Big Data helps with Wildlife Conservation
Big Data in Healthcare- Real World Use-cases
Big Data in Retail industry- Real World Uses
Real Time Big Data Applications
Top 10 Latest Big Data Trends
Try R for Data Science
Data Analytics Tutorial
R vs SAS vs SPSS- Data Analytics Tools
Exporting Data from R to CSV, Text, SAS, SPSS, Stata
Importing Data in R
Manipulating and Processing Data in R
R Descriptive Statistics
Contingency Tables in R
R Data Types – Vectors, Matrices, Lists, Data Frames
Graphical Data Analysis with R
Hypothesis Testing In R
R Linear Regression Tutorial
R Nonlinear Regression Analysis
R Decision Trees
Cluster Analysis with R
Graphical Models in R
Top Real-World Graphical Models Applications
SVM Training and Testing Models in R with e1071
Bayesian Networks with R
Bayesian Methods with R
Probabilistic Inference in Bayesian Networks
Top 10 Real-World Bayesian Network Applications
R- Predictive and Descriptive Analytics
R Data Frames & Operations
Logistic Regression in R
Binomial and Poisson Distributions in R
Numeric and Character Functions in R
Random Forest in R
Lattice Package in R with Lattice Graphs
R Statistics for Statistical Programming in R
Principal Components and Factor Analysis in R
Bootstrapping in R & Bootstrap Resampling
OLS Regression in R
T-Tests in R- Welch t-test and its uses
ANOVA Model in R – Common Statistical Models
Survival Analysis in R Programming
Normal Distribution in R – Basic Probability distribution
R Cluster Analysis
Data Visualization in R
Classification in R
R Data Reshaping – Reshape Function and Reshape Package
Exploratory Data Analysis With R
Data Analytics with R, Tableau, and Excel
Chi-Square Test in R
Bar Charts in R | Histogram in R
Top 10 Data Analytic Tools
Best Data Analysis Software Systems
R Vs Python for Data Science and Statistics
Do it with SAS (Pun intended)
Special & Built-in Data Sets in SAS
Entering and Reading Raw Data in SAS
Writing Raw Data in SAS – PROC Export & CSV file
Merging Datasets in SAS
SAS Concatenate Data Sets with Set Statement
SAS SQL – PROC SQL SAS
SAS Proc Sort Data Sets
SAS Formats- Built-in & User-Defined
Splitting and Subsetting Datasets in SAS
SAS Histogram Statement With UNIVARIATE Procedure
SAS Bar Charts – Simple, Stacked & Clustered
SAS Pie Charts | Types of Pie Charts
SAS ODS (Output Delivery Systems)
SAS Boxplots – PROC SGPANEL, SGPLOT & Types
SAS Scatter Plots & Types
SAS Correlation Analysis- PROC CORR & Correlation Matrix
SAS Linear Regression with PROC REG Procedure
SAS PROC T-test
SAS Cross Tabulation of Two or More Variables
Frequency Distribution Using SAS PROC FREQ
SAS Standard Deviation
Arithmetic Mean – SAS PROC MEANS
SAS Hypothesis Testing
One Way ANOVA – SAS PROC ANOVA
Chi-Square Test – SAS PROC FREQ
SAS Bland-Altman Analysis & Plot
SAS Repeated Measure Analysis – PROC GLM Procedure
Fishers Exact Test in SAS Using PROC FREQ Procedure
FIRST. and LAST. Variables in SAS
Learn all about Machine Learning
Machine Learning Tutorial
Machine Learning Software
Applications of Machine Learning
Future of Machine Learning
ML Advantages & Limitations
Algorithms for ML
Artificial Neural Networks (ANN) – ML
Convolutional Neural Networks (CNN) – ML
Recurrent Neural Networks(RNN) – ML
ML ANN Applications
ML ANN Learning Rules
ML ANN Model
ML ANN Algorithms
Deep Learning Tutorial- ML
ML-DL Terminologies
ML-DL For Audio Analysis
Support Vector Machine(SVM) – ML
SVM Applications – ML
SVM Kernel Functions – ML
Dimensionality Reduction – ML
ML Gradient Boosting Algorithm
ML XGBoost Tutorial
ML XGBoosting Algorithm
ML AdaBoost Algorithm
Deep Learning vs Machine Learning
DL vs ML vs AI vs DS
Python Machine Learning Tutorial
Python Machine Learning Environment Setup
Data Preprocessing for Python Machine Learning
Train & Test Sets for Python Machine Learning
Python ML Techniques
Python ML Applications
Python ML Algorithms
Python Deep Learning Tutorial
Python DL Applications
Python DL Environment Setup
Python Libraries for Deep Learning
Neural Network for DL with Python
Computational Graphs- Python DL
Introduction To Bayesian Methods
Bayesian Networks and their Characteristics
Probabilistic Bayesian Network Inference
Bayesian Network Applications
Data Science Interview Questions- Part 1
Data Science Interview Questions- Part 2
Data Science Interview Questions- Part 3
Data Science Interview Questions- Part 4
Data Science Interview Questions- Part 5
SAS Interview Questions- Part 1
SAS Interview Questions- Part 2
SAS Interview Questions- Part 3
Check out more cool technologies
Dig deeper with Data Mining (Pun intended)
Introduction to Data Mining
19 Data Mining Tools
Data Mining Architecture
Data Mining Limitations
Data Mining Terminologies
Data Mining Applications
Best Data Mining Books
The Data Mining Process
6 Data Mining Techniques
Text Mining in Data Mining
Data Mining Software Systems
Data Analysis Software Systems
Benefits of Data Mining in Machine Learning
Data Mining Algorithms
Data Mining Clustering
Data Mining- Knowledge Discovery Database (Kdd)
Data Mining Query Language (DMQL)
Data Mining Interview Questions- Part 1
Data Mining Interview Questions- Part 2
Data Mining Interview Questions- Part 3
Create with Artificial Intelligence
Introduction to Artificial Intelligence
Pros and Cons of AI
Future of AI & Career Opportunities
Neural Networks in AI
Transfer Learning for Deep Learning with CNN
Popular Search Algorithms in AI
DS vs AI vs ML vs DL
DL vs ML
Best AI Books
Python AI Tutorial
Python AI NLP Tutorial
Python AI NLTK Tutorial
Python AI Speech Recognition
Python AI Heuristic Search
Python AI Logic Programming
Python AI Reinforcement Learning
Applications of AI
Fuzzy Logic Systems with AI
Expert Systems with AI
AI- Natural Language Processing
Robotics with AI
Best AI Software
AI Interview Questions- Part 1
AI Interview Questions- Part 2
Exploring the Concept
Let’s take a look at some facts about Data Science and its philosophies.
William S. Cleveland introduced data science as an independent discipline in 2001. This came with his article “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics”. According to him, six technical areas aggregate into the field of data science: multidisciplinary investigations, models and methods for data, computing with data, pedagogy, tool evaluation, and theory. In a 2012 article, DJ Patil claimed the coinage of the term along with Jeff Hammerbatch as a way to define their jobs at LinkedIn and Facebook, respectively.
Today, data science puts to use scientific methods, processes, algorithms, and systems hoping to extract knowledge and insights from data in forms structured and unstructured.
William S. Cleveland