Data Science

Data Science training in Vizag


Course Description

Data science is a field that applies principles and techniques of data analysis, machine learning, and statistics to gain insights and understanding from data-related events. In today’s job market, many aspire to become data scientists, making data science training one of the most popular courses to pursue. Regardless of the industry, employers are actively seeking skilled data scientists who can provide valuable business insights. Consequently, it is currently one of the most sought-after courses, with companies willing to offer substantial salaries to individuals who have undergone data science training. Data science is also used to analyze historical data and predict potential risks for companies, enabling proactive risk mitigation.

data Science training in Vizag

Data Science training in Vizag

Numerous online websites and offline coaching centers offer data science training. Online training institutes like JNNC Technologies stand out for providing high-quality training aligned with industry requirements, experienced trainers, real-world industry projects, and certification. The training also covers visualization and reporting tools. Those who cannot commit to regular training sessions, including working professionals looking to change careers, can benefit from self-paced data science learning opportunities. This self-learning approach features an updated curriculum in line with current industry needs and best practices, and it includes the guidance of experienced data science professionals who troubleshoot real-world data science issues.

Can you acquire data science skills at your own pace through online training? Yes, you can certainly learn data science through self-study. There are online resources and videos that can provide foundational knowledge about data science. However, self-learning online can be a bit challenging. Fortunately, online courses like the one offered by JNNC Technologies provide a supportive environment with online trainers available to assist with any queries. The data science self-learning online course is designed especially for busy individuals who cannot attend physical data science classes. This self-paced program accelerates the learning process and is suitable for working professionals who wish to take charge of their learning journey.

data Science training in Vizag

Data Science training in Vizag

Alternatively, individuals can utilize online training resources specifically designed to provide comprehensive knowledge about data science. These courses are in high demand and cater to individuals seeking a deep understanding of data science concepts. Contrary to popular belief, self-training can be just as effective as learning from an expert, provided the learner possesses the enthusiasm and determination to become a self-taught data science professional.

Given the importance of data science training, individuals who embark on this journey can fast-track their careers, securing lucrative job roles and advancing to the next level professionally. By enrolling in JNNC Technologies data science program, you can access top-notch training in this field.

Data Science training in Vizag

Course Curriculam


Overview of Data Science

Module 1: Statistics and Probability

a) Descriptive Statistics:

  • Central tendency: Mean, Median, Mode
  • Sample variance
  • Standard deviation
  • Random Variables: Discrete, Continuous
  • Probability density functions
  • Binomial distribution
  • Expected Value, E(X)
  • Poisson Process
  • Law of large numbers
  • Standard normal distribution and empirical rule
  • Z-score

b) Inferential Statistics:

  • Central limit theorem
  • Sampling distribution of the sample mean
  • Standard error of the mean
  • Mean and variance of Bernoulli distribution
  • Margin of error 1
  • Margin of error 2
  • Confidence interval
  • Hypothesis testing and p-value
  • One-tailed and two tailed tests
  • Z-statistics and T-statistics
  • Type 1 error
  • Squared error of regression line
  • Co-efficient of determination
  • Chi-square distribution
  • Pearson’s chi square test (goodness of fit)
  • Co-relation and casualty.

Module 2: Data Analysis using Numpy and Pandas

1. Numpy

  • Numpy Numpy Vector and Matrix
  • Functions – arrange(), zeros(), ones(), linspace(), eye (),
  • Reshape(), random(), max(), min(),
  • argmax(), argmin(), shape and dtype attribute
  • Indexing and Selection
  • Numpy Operations – Array with Array, Array with Scalars,
  • Universal Array Functions


  • Pandas Series
  • Pandas Data-Frame
  • Missing Data (Imputation)
  • Group by Operations
  • Merging, Joining and Concatenating Data-Frame.
  • Pandas Operations
  • Data Input and Output from wide variety of formats like csv, excel, db and html etc.

Module 3: Data Visualization using Matplotlib, Seaborn, Pandas-in built, Plotly and Cufflinks


  • plot() using Functional approach
  • multi-plot using subplot()
  • plt.figure() using OO API Methods
  • add_axes(), set_xlabel(), set_ylabel(), set_title() Methods
  • Customization – figure size, impoving dpi, Plot appearance,
  • Markers, Control over axis appearance and special Plot Types


  • Distribution Plots using distplot(), jointplot(), pairplot(), rugplot(),
  • kdeplot()
  • Categorical Plots using barplot(), countplot(), boxplot(), violinplot(),
  • stripplot(), swarmplot(), factorplot()
  • Matrix Plots using heatmap(), clustermap()
  • Grid Plots using PairGrid(), FacetGrid()
  • Regression Plots using lmplot()
  • Styles and Colors customization.

3. Plotly and Cufflinks

  • Interactive Plotting using Plotly and Cufflinks

4.Pandas Built-in

  • Histogram, Area Plot, Bar Plot, Scatter Plot, Box-plot, Hex-plot, Kde-plot, Density Plot e. Choropleth Maps
  • Interactive World Map and US Map using Plotly and Cufflinks Module

Module 4: GIT

  • Distribution Version Control System
  • How internally, GIT Manages Version Control on Changesets.
  • Creating Repository
  • Basic Commands like, git status, git add, git remove, git branch, git checkout, git log, git cat-file, git pull, git push, git commit
  • Managing Configuration – System Level, User Level, Repository level

Module 5: Jupyter Notebook

  • Introduction, Basic Commands, Keyboard Shortcut and Magic Functions

Module 6: Linear Algebra and Calculus

  • Vector and Matrix, basic operations
  • Trigonometry
  • Derivatives

Module 7: SQL

  • MySQL Server and Client Installation
  • SQL Queries
  • CRUD Operations

Module 8: Big Data

  • What is big data?
  • What is distributed computing?
  • What is parallel processing?
  • Why data scientist require big data?

Module 9: Machine Learning Introduction

  • What is Machine Learning
  • Machine Learning Process Flow-Diagram
  • Different Categories of Machine Leaning – Super- vised, Unsupervised and Reinforcement
  • Scikit-Learn Overview
  • Scikit-Learn cheat-sheet

Module 10: Regression

  • Linear Regression
  • Robust Regression (RANSAC Algorithm)
  • Exploratory Data Analysis (EDA)
  • Correlation Analysis and Feature Selection
  • Performance Evaluation – Residual Analysis, Mean Square Error (MSE), Co-efficient
  • Determination R^2, Mean Absolute Error (MAE), Root Mean Square Error (RMSE)
  • Polynomial Regression
  • Regularized Regression – Ridge, Lasso and Elas- tic Net Regression
  • Bias-Variance Trade-Off
  • Cross Validation – Hold Out and K-Fold Cross Validation
  • Data Pre-Processing – Standardization, Min-Max, Normalization and
  • Binarization
  • Gradient Descent


  1. Predicting Boston House Prices – bostonhoustingmlnd
  2. Ecommerce Project – Company want to decide whether to focus their efforts on Mobile Experience or Website Experience.
  3. 4 USA Housing Prediction Project.
  4. New York City Taxi Fare Prediction – fareprediction
  5. Emergency 911 Calls – mchirico/montcoalert

Module 11: Classification – Logistic Regression

  • Sigmoid function
  • Logistic Regression learning using Stochastic Gra-dient Descent (SGD)
  • SGD Classifier
  • Measuring accuracy using Cross-Validation, Strati-fied k-fold
  • Confusion Matrix – True Positive (TP), False Posi-tive (FP), False
  • Negative (FN), True Negative (TN)
  • Precision, Recall, F1 Score, Precision/Recall Trade-Off
  • Receiver Operating Characteristics (ROC) Curve.


  1. Digit Recognizer –
  2. Titanic: Machine Learning from Disaster –
  3. Advertising Project – Indicating whether a particular internet user will click on an advertisement or not.
  4. Project on working on classified Data to predict the Target Class 0 or 1.
  5. Another, Project on working on classified Data to predict the Target Class 0 or 1.

Module 12: Classification – k-Nearest Neighbor(KNN)

  • Classification and Regression
  • Application, Advantages and Disadvantages
  • Distance Metric – Euclidean, Manhattan, Cheby- shev, Minkowski
  • Measuring accuracy using Cross-Validation, Stratified k-fold, Confusion Matrix, Precision, Recall, F1-score.


  1. Breast Cancer Wisconsin (Diagnostic) Pro-ject using KNN-
  2. Iris Species – uciml/iris

Module 13: Classification – SVM (Support Vector Machine)

  • Classification and Regression
  • Separating line, Margin and Support Vectors
  • Linear SVC Classification
  • Polynomial Kernel – Kernel Trick
  • Gaussian Radial Basis Function (rbf)
  • Grid Search to tune hyper-parameters
  • Support Vector Regression

data Science training in Vizag


  1. Breast Cancer Wisconsin (Diagnostic) Project using KNN –
  2. Iris Species –

Module 14: Classification –Decision Trees

  • CART (Classification and Regression Tree)
  • Advantages and Disadvantages and its applications.
  • Decision Tree Learning algorithms – ID3, C4.5, C5.0 and CART.
  • Gini Impurity, Entropy and Information Gain
  • Decision Tree Regression
  • Visualizing a Decision Tree using graphviz module.
  • Regularization using tuning hyper-parameters using GridSearch CV.


1.IBM HR Analytics Employee Attrition and Per-formance –

2.Restaurants Data –

3.Predicting Bank Marketing Analysis -

4.FIFA 18 Complete Player Dataset –


Module 15: Classification – Ensemble Methods

  • Bootstrap Aggregating or Bagging
  • Random Forest algorithm
  • Extremely Randomized (Extra-Trees) Ensemble
  • Boosting – AdaBoost (Adaptive Boosting), Gradient Boosting
  • Machine (GBM), XGBoost (Extreme Gradient Boosting)

Module 16: Unsupervised Learning – Clustering

  • Connectivity- based Clustering using Hierarchical Clustering.
  • Ward’s Agglomerative Hierarchical Clustering
  • K-Means Clustering
  • Elbow Method and Solhouette Analysis


1. Lending Club Loan Data Analysis – data

2.U.S. News And World Report’s College Data – world-reports-college-data

3.Credit Card Dataset for Clustering –

Module 17: Unsupervised Learning – Dimensionality

  • Linear Principal Component Analysis (PCA) reduction.
  • Kernel PCA
  • Linear Discriminant Analysis (LDA) on Supervised Data.


1.Breast Cancer Wisconsin (Diagnostic) Analysis us-ing PCA – -data

2.Predicting Abalone’s Sex – https://

3.Wine Project – wine-reviews

4.SMS Spam Collection Dataset Analysis – collection- dataset

5.Auto Summarizing Text using Rule Based Model.

6.Yelp Business Rating Prediction – https://

Module 18: Model Deployment on AWS Cloud

  • What is cloud computing?
  • What is AWS?
  • How to store data in AWS S3?
  • Create deep learning instance on EC2.
  • Amazon sage maker to train, tune, build and deploy on production.




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