Data Science Training

E school cart providing 100% Result Oriented Data Science Training in Hyderabad this course duration is 3 months. Data Science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured. The most important interdisciplinary field of all is Machine Learning (a branch of Artificial Intelligence). Simply speaking, Machine Learning is the field of study, that gives computers the ability to learn from data, without being explicitly programmed.

" Push yourself, Because no one else is going to do IT for You — Success Quote "

The course we will cover all necessary concepts to make a successful Data Scientist. The concepts we cover are Descriptive Statistics, Inferential Statistics, Basic Python, Pandas, NumPy, SciPy, Statistical Data Analysis, StatsModels, Scikit-Learn, Mathematics behind Machine Learning Algorithms (Gradient Descent, SVM, Kernel SVM, etc.), error analysis and most of the accuracy measures, techniques of fine tuning a model.

Course Location Mode of Class Duration
Data Science Hyderabad Class-Room/Online 3 Months

Data Science Course Highlights

Trainer with 10+ years of experience

Certified Trainer

Real Time Project Examples

Exercises after every Topic

Trainer support after completion of the course

Placement Assistance

Trainers with Industry Experience and IIIT Hyderabad

All Concepts and ML/DL Algorithms with Code Examples

Data Science Course Syllabus

Introduction to Data Science/Data Analytic

  • What is Data Science?
  • Why Data Science?
  • Applications of Data Science
  • How much of statistics?
  • How much of mathematics?
  • How much demand in IT (across all) industry?

Descriptive Statistics

  • Central Tendency (mean, median and mode)
  • Interquartile Range
  • Variance
  • Standard Deviation
  • Z-Score/T-Score
  • Co-variance
  • Correlation

Data Distributions

  • Central Tendency (mean, median and mode)
  • Binomial Distribution
  • Introduction to Probability
  • Normal Distribution

Overview of Data Visualization

  • Bar Chart
  • Histogram
  • Box whisker plot
  • Dot-plot
  • Line plot
  • Scatter Plot

Introduction to Python

How to install python (anaconda)

  • How to work with Jupyter Notebook
  • How to work with Spyder IDE
  • Compound data types
  • Strings, Lists, Tuples, Sets, Dictionaries
  • Control Flows
  • Keywords (continue, break, pass)
  • Functions (Formal/Positional/Keyword arguments)
  • Predefined functions (range, len, enumerate, zip)

Introduction to NumPy

  • One-dimensional Array
  • Two-dimensional Array
  • Predefined functions (arrange, reshape, zeros, ones, empty, eye, linspace)
  • Basic Matrix operations
  • Slicing, indexing, Looping, Shape Manipulation, Stacking
  • Scalar addition, subtraction, multiplication, division, broadcasting
  • Matrix addition, subtraction, multiplication, division and transpose, broadcasting

Introduction to Pandas

  • Series
  • DataFrame
  • df.GroupBy
  • df.crosstab
  • df.apply

Inferential Statistics

  • Central Limit Theorem
  • Confidence Interval and z-distribution table
  • Statistical Significance
  • Hypothesis testing
  • P-value
  • One-tailed and Two-tailed Tests
  • Chi-Square Goodness of Fit Test
  • F- Statistic (ANOVA)
  • Skewness, Kurtosis

Exploratory Data Analysis

  • Train/Test split – Data snooping bias
  • Statistical Data Analysis
  • Fixing missing values
  • Finding outliers
  • Data quality check
  • Feature transformation
  • Data Visualization (Matplotlib, Seaboarn)
  • Categorical to Categorical
  • Categorical to Quantitative
  • Quantitative to Quantitative
  • Bi-Variate data analysis (Hypothesis Testing)
  • Categorical and Quantitative (ANOVA)
  • Categorical to Categorical (Chi-Square)
  • Quantitative to Categorical (Chi-Square)
  • Quantitative to Quantitative (Correlation)

Intro to Regression (Supervised Learning)

  • What is regression?
  • Simple linear regression
  • Linear Regression – a statistics perspective (statsmodels – OLS)
  • Evaluation metrics (R-Square, Adj R-Square, MSE, RMSE)

Regression Analysis (ML – statsmodels)

  • Mean centralization and its use in multiple linear regression
  • Mean centralization and its use in multiple linear regression
  • Multiple linear regression
  • P – Value based feature selection methods (Backward, Forward and Mixed)
  • Linear regression assumptions (linear relations – fitted vs residuals plot, homoscedasticity, normal distribution of the error term, serial correlation, multicollinearity)
  • Q-Q Plot, Shapiro Wilk test – different ways to check normality of data.
  • Data transformation techniques

Encoding & Code Modularization

  • Label Encoding
  • One-Hot (dummy variable) encoding
  • Dummy variable trap
  • Scikit-Learn → Custom Transformers
  • Scikit-Learn → Pipeline

Multiple Linear regression (scikit-learn)

  • Normal Equation (Linear Algebraic way of solving linear equation)
  • Gradient Descent (Calculus way of solving linear equation)
  • Multiple Linear Regression (SGDRegressor)
  • Feature Scaling ( Min-Max vs Mean Normalization)
  • Feature Transformation
  • Polynomial Regression

Bias-Variance trade off, Re-sampling Techniques

  • Bias-Variance trade off
  • Major challenges in Data Science project (Data or Algorithm).
  • Hold-out Data
  • K-fold Cross-Validation
  • Leave-one-Out
  • Random Sub-sampling Cross-Validation
  • Bootstrapping

Model Evaluation, Model Selection, Polynomial Regression, Regularization

  • Train/Validation/Test split
  • K-Fold Cross Validation
  • The Problem of Overfitting (Bias-Variance trade-off)
  • Learning Curve
  • Regularization (Ridge, Lasso and Elastic-Net)
  • Feature selection
  • Hyper Parameter Tuning (GridSearchCV, RandomizedSearchCV)

Model Deployment

  • Pickle (pkl file)
  • Model load from pkl file and prediction

Classification (Supervised Learning)

  • Logistic Regression Algorithm (SGD Classifier)
  • Accuracy measurements – handling imbalanced dataset
  • Accuracy score
  • Confusion matrix
  • Precision
  • Recall
  • Precision – Recall tradeoff curve
  • ROC curve
  • AUC score
  • Multi-class Classification
  • One-vs-One
  • One-vs-All
  • Softmax regression classifier
  • Multi-label Classification
  • Multi- output Classification

Support Vector Machine

  • SVM Classifier (Soft/Hard – Margin)
  • Linear SVM
  • Non-Linear SVM
  • Kernel Trick (mathematics behind kernel trick)
  • Kernel SVM
  • SVM Regression

Clustering (Unsupervised Learning)

  • K-means
  • Hierarchical
  • How to use unsupervised outcome as support to solve supervised problem

Dimensionality Reduction (Unsupervised)

  • PCA
  • Math behind PCA – Eigenvectors, eigenvalues, covariance matrix
  • Choosing Right Number of Dimensions or Principal Components
  • Incremental PCA
  • Kernel PCA

Tree Based Algorithms

  • Regression Trees vs Classification Trees
  • Entropy
  • Gini Index
  • Information Gain
  • Tree pruning

Ensemble models

  • Voting Classifiers (Heterogeneous Ensemble Models)
  • Homogeneous Ensemble Models
  • Random Forest
  • Bagging
  • Pasting
  • Introduction to Boosting (Ada, Gradient)

Naive Bayes

  • Bayes Theorem
  • Naive Bayes Algorithm
  • Introduction to Text Analytics
  • Tokenization
  • Text Normalization, stemming, lemmatization
  • Bag of words mode

Anomaly Detection

  • Anomaly vs Classification
  • Credit Card Fraud detection – Anomaly Detection Algorithm
  • Assumptions of normality

Introduction to Hadoop & PySpark

  • Overview of Hadoop architecture
  • Overview of YARN architecture
  • Map-Reduce example
  • Overview of Spark Context (–master YARN)
  • Resilient Distributed Datasets (RDDs)
  • RDD Operations (Transformations, Actions)
  • Spark DataFrames
  • Spark ML model with Pipeline
  • Classification model, MulticlassMetrics

Introduction to Neural Networks

  • Perceptron, Sigmoid Neuron
  • Neural Network model representation
  • How it works
  • Forward-Propagation
  • Back-Propagation

Data Science Course Training FAQs

Why Data Science/AI is so popular now?

  • Increase in computing power and cheap memory
  • Distributed computing technologies such as Hadoop
  • Availability of ML/AI/Deep Learning algorithms implemented in different languages such as R, Python, Scala, etc.(Scikit-Learn, TensorFlow, tools like R, Matlab, Spark)

Does one need to have some computer programming knowledge ?

  • It is better to have minimum programming knowledge
  • We are anyway providing necessary programming skills to execute Data Science projects

Explain course design and duration?

  • The course will make you a solution provided for the real-time Data Science problems
  • We start with Statistics, Basics of Python, data analysis with Pandas, NumPy, after this we will get deeper into Machine Learning, Error analysis and Fine tuning models
  • The course duration is of three months, every day one hour. Monday to Friday

Can we attend class online ?

  • Yes, we broadcast live class over the internet. One could join the class either in classroom or online
  • We are also providing recorded videos for further reference

Who can become a Data Scientist ?

  • Business Analysts and Data Analysts.
  • Database professionals, Developers, Leads, Managers from Information Technology industry
  • Fresh graduates who wish to make a career in Data Science, Machine Learning, Statistical Data Analysis and Artificial Intelligence

What are the prerequisites to join Data Science course ?

  • We will cover all that is needed to make you a Data Scientist (Statistics, Mathematics, Machine Learning, Python, etc..)
  • Only requirements are, should have studied mathematics at 10 + 2 (Intermediate) level. Good intuition and logic

How soon I can get a job as per current market situation?

  • We would say, within one month of finishing the course. This depends on below conditions
  • The course duration will take three months, we impart all concepts with utmost clarity and depth. The course material covers answers for all most all interview questions. This will build a lot of confidence to face interview. It requires dedication and a lot of practice of the concepts we taught. Practice makes perfect