What is Data Science?

Have you come across a situation while you were shopping online and were surprised to find advertisements related to your online searches of Facebook or other social media platforms for over a week? Data Science has a huge role to play in this.

Firms are now utilizing Data Science to improve the shopping experience of targeted customers while making buying decisions through process failure prediction. Plus, businesses stress on Data Analytics skills, as they have realized the significance of data to facilitate innovation and take strong decisions that are driven by data. It also involves mining the data insights based on interpretation, behavior, trends and the results for supporting the decision making process essential for business growth.

The professionals performing such data mining activities are called as a Data Science professional or Data Scientist. This is why many professionals and students are keen to be a part of the Data Science & Analytics Course in Singaporeby Web Stack technologies.

Why Should You Take Up An Online Class On Data Science?

The arena of Data Science is considered to be a highly popular profession in demand and is the most sought after job role across the world. There are numerous benefits that you will get when you take up the Online Data Science Classes in Singapore at Web Stack Technologies. They are:

  • Best Career Option: Data Science is considered as the No.1 career choice since the year 2018, as it has a fast-paced scope for excellent career growth
  • Best Paying Job: The projected earning range of Data Scientists is up to $104,000 per year.
  • Growing Demand : Career experts opine that would be an immense increase in data job opportunities in the coming years.

Why Choose Web Stack Technologies For Data Science Online Classes?

Our Web Stack Technologies conducts Data Science online classes from Singapore with a Singaporean faculty with more than 5 years of experience in making Data Science learning process an enjoyable one. It doesn’t matter if you are a beginner or an expert coder, be a part of our Online Data Science Classes in Pune today and stay updated with the market trend. When you take up our online Data Science classes, you will achieve hands-on training in the implementation of several industry based and real-life projects in varied domains, such as insurance, retail, healthcare and so on.

Why Web Stack Technologies Is the Best Online Data Science Training Institute?

Web Stack Technologies specializes in offering Online Data Science Classes in Nagpur,which is a highly comprehensive course on Data Science in the market. Our data Science online course covers a wide range of concepts right from Data Mining, Data Transformation, Data Extraction, Data Integration, Data Collection, Data Visualization, developing Prediction models, Data Cleansing, Data Exploration and Feature Engineering.

Our Data Science course will also feature tools and skills, such as Statistical Analysis, Neural Networks, Text Mining, R Studio, Regression Modelling, Spark, Hypothesis Testing, Hadoop, Predictive Analytics, Machine Learning, Predictive Modelling, Natural Language Processing, Tableau, and Deep Learning. We also conduct Online Python Programming Classes and teach R programming languages.

Who Can Take Up The Online Data Science Classes?

Those who are planning to climb up their career ladders can be a part of our Online Data Science Classes. The following people are eligible for our online Data Science classes from Singapore:

  • Working professional from any niche with analytical, logical and mathematical skills
  • People dealing with Data Warehousing, Business intelligence, reporting tools. etc.
  • Mathematicians, Economists and Statisticians
  • Six Stigma professionals
  • Business Enthusiasts and Analysts
  • Software programmers
  • Fresher from any streams possessing logical and analytical skills


1. Introduction to Analytics

2. Data Science Using Python

3. Statistical Methods for Decision Making

4. Supervised Learning Regression

5. Supervised Learning Classification

6. Unsupervised Learning

7. Ensemble Techniques

8. Text Mining and Sentiment Analysis

9. Time Series Forecasting

10. Database Management System

11. Data Visualization using Tableau and Qlik Sense

  • Introduction to programming using Python
  • Syntax and Semantics of Python programming
  • Loops
  • Conditional statements
  • User-defined functions
  • NumPy
  • Pandas
  • Matplotlib
  • Seaborne
  • Exploratory Data Analysis
  • Pandas
  • Summary statistics (mean, median, mode, variance, standard deviation)
  • Seaborne
  • Matplotlib
  • Statistical Methods for Decision Making
  • Probability distribution
  • Normal distribution
  • Poisson's distribution
  • Bayes’ theorem
  • Central limit theorem
  • Hypothesis testing
  • One Sample T-Test
  • Anova and Chi-Square
  • SQL Programming
  • Introduction to DBMS
  • ER diagram
  • Schema design
  • Key constraints and basics of normalization
  • Joins
  • Subqueries involving joins and aggregation
  • Sorting
  • Independent subqueries
  • Correlated subqueries
  • Analytic functions
  • Set operations
  • Grouping and filtering
  • Linear and Logistic Regression
  • Multiple linear regression
  • Fitted regression lines
  • AIC, BIC, Model Fitting, Training and Test Data
  • Introduction to Logistic regression, interpretation, odds ratio
  • Misclassification, Probability, AUC, R-Square
  • Supervised Learning Classification
  • CART
  • KNN (classifier, distance metrics, KNN regression)
  • Decision Trees (hyper parameter, depth, number of leaves)
  • Naive Bayes
  • Unsupervised Learning
  • Clustering - K-Means & Hierarchical
  • Distance methods - Euclidean, Manhattan, Cosine
  • Features of a Cluster - Labels, Centroids, Inertia
  • Eigen vectors and Eigen values
  • Principal component analysis
  • Ensemble Techniques
  • Bagging & Boosting
  • Random Forest
  • AdaBoost & Gradient boosting
  • Time Series
  • Trend and seasonality
  • Decomposition
  • Smoothing (moving average)
  • SES, Holt & Holt-Winter Model
  • AR, Lag Series, ACF, PACF
  • ADF, Random walk and Auto Arima
  • Text Mining
  • Text cleaning, regular expressions, Stemming, Lemmatization
  • Word cloud, Principal Component Analysis, Bigrams & Trigrams
  • Web scrapping, Text summarization, Lex Rank algorithm
  • Latent Dirichlet Allocation (LDA) Technique
  • Word2vec Architecture (Skip Grams vs CBOW)
  • Text classification, Document vectors, Text classification using Doc2vec
  • Data Visualization
  • Building interactive dashboards using Tableau
  • Data Visualization using Tableau