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Data Science Training

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Data Science COURSE

About this course

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Data Science Training by Sathya Technologies lets you build and master skills like Descriptive and Inferential Statistics, Probability Distribution, Prediction Analytics using tools R Studio, Data Visualization, SQL, SAS, Hadoop etc. The course is aimed at preparing you to take up the role of Data Scientist working with huge amount of data to make prediction models using various statistical concepts. Using Machines Learning concepts and modeling tools Data Scientist should help organizations process large amount of unstructured data and gain information to shape Business goals.

Why This Course?

  • Average Salary of a Data Scientist is $120,931 PA
  • Demand for Data Scientist will zoom to 28% by 2020 across all industry verticals.
  • Financial Institutions, Insurance Sectors, FMCG companies depend on Data Scientist to shape their Business Goals and Strategies.

Best Data Science Training in Ameerpet, Hyderabad

Data Scientist is a person who employs various methods and tools to extract meaningful data . They deal with huge amounts of data and make predictions using statistical concepts. They have to formulate and write queries and derive information from raw data.

At Sathya Technologies we begin with learning in detail about inferential statistics. Proceeding further we learn process of data science workflow. WE learn bow to collect, explore, model and validate data using various prediction analysis tools.

Course Objective

  • To learn key features of Data Science.
  • ‘Understand the probability distributions in details.
  • Working with real time problems.
  • To work on data handling concepts.
  • Working on integrating with other tools.

How the program will be conducted

Sathya Technologies with its start-of- art class rooms and Lab infrastructure at Ameerpet Hyderabad offer the best and most conducive learning environment, with a team of highly skilled trainers having years of industry experience.  Classroom trainings will be conducted on a daily basis. Practical exercises are provided for the topics conducted on daily basis to be worked upon during the lab session.  Online session conducted through the virtual classroom also have the same program flow with theory and practical sessions.  Our Labs can be accessed online from across the world allowing our online training student to make the best use of the infrastructure from the comfort of their home.

Career Opportunities in Data Scientist

With the popularity of Big Data increasing exponentially, opportunities as Data Scientist / architects has been growing in all major industry sectors .etc.  Training programs on Data Science technology by Sathya Technologies focuses on empowering the students with the latest concepts and industry specific topics.Our well experienced trainer and well planned course materials ensures for 100% success in interviews.

Who can learn?

Targeted Audience

  • Software Developers
  • Statisticians
  • College / Fresher’s with statistics and math background
  • Statistics Professionals

Prerequisite to learn the course

Experience in Statistics Machine Language will help becoming Data Scientist. Understanding business and domain concepts would be added advantage. Basic R programming concepts will come in handy. Knowledge in BigData would also be helpful.

  1. Need of Data Science
  2. History of Data Science
  3. Whatis Data Science
  4. Data Science vs Data Analytics
  5. Whatis DataAnalytics
  6. Whatis Data Analysis
  7. DataMining
  1. What is machine learning
  2. Types of learning
  3. Sup°rvised Machine Learning
  4. Unsupervised Machine Learning
  5. Machine learning algorithms
  6. Flow of Supervised and Unsupervised Machine Learning
  7. Simple linear Regression              ”
  8. Multiple Linear Regression
  9. Logistic Regression
  10. K-Nearest Neighbour
  11. Support Vector Machine
  12. Decision Tree
  13. Random forest
  14. Ensemble Machine Learning
  15. naive Bayes
  16. Clustering
  17. K-Means
  18. Hierarchical Clustering
  • Whatismachine learning
  • Types of learning
  • Sup°rvised Machine Learning
  • Unsupervised fvlachine Learning
  • Mechine learning algorithms
  • Flow of Supervised and Unsupervised Machine Learning
  • Simple linear Regression              ”
  • Multiple Linear Regression
  • Logistic Regression
  • K-Nearest Neighbour
  • Support Vector Machine
  • Decision Tr”ee ”
  • Random r-orest
  • Ensemble Machine Learning
  • NaTve Bayes
  • Clustering
  • K-Means
  • Hierarchical Clustering
  • Data Science Essentials
  • Numpy
  • Introduction
  • Numpy Package
  • Ndarray Object – “
  • Data Types
  • ArrayAttributes
  • Array from Numerical Ranges
  • Indexing & Slicing
  • Advanced Indexing
  • lterating over array
  • String Functions
  • Arithmetic Operations
  • Statistical Functions
  1. Introduction
  2. Pandas Package
  3. Series
  4. Data Frame
  5. Panel
  6. Descriptive Statistics
  7. Indexing and Selecting Data
  8. Itaration
  9. Sorting
  10. Aggregations
  11. Missing Data
  12. GroupBy
  13. Merging/Joining
  14. Concatenation
  15. DataFunctonality
  16. Pandas-Visualization
  17. Pandas- IO Tools
  18. CSVto DataFrame
  19. Locandiloc
  20. DafaFrame Filtering
Manipulating DataFrames with Pandas
  1. Extracting and Transforming Data
  2. Neshapng Data
  3. Grouping Data
Data Visualization using Python
  • matplotlib
  • Bar Graph
  • Histogram
  • Scatter Plot
  • Pie Chart
Statistic and Mathematical Essentials for Data Science
  1. feature of Central Tendency
  2. Mean
  3. Mode
  4. Median
  5. Range
  6. Inter Quartile Range
  7. Variance
  8. Standard Deviation
  9. Correlation
  10. Regression models in Machine Learning
  11. Residuals
  12. Correlation Coefficients ( Pearson)
  13. Accuracy Measurement
  14. Least Square Regression
  15. Root Mean Square Error
  16. Coefficient of Determination (R2 Score)
  17. Cost Function
  18. Gradient Descent
  19. Hypothesis Tes lint and p-values
  20. T-values
  21. Z-score
  22. Create Dummy Variables
  23. Cross Validation
  24. Confusion Matrix
  25. Compete Precision, Recall, F-Measure and support
  26. TPR, FPR, FNR, TNR
  27. Accuracy
  28. Learning rate
  29. Sensitivity and Specificity
  30. ROC Curve
  31. (Receiver Operating Characteristic)
  32. Receiver Operating Characteristic
  33. Calculating similarity  based on Euclidean/Manhattan Distance
  34. Calculation of Entropy and Information Gain
  35. Calculation of Gini index
  36. Basicprobability
  37. Randomness
  38. Conditional Probability
  39. Naive BayesTheorem
  40. Multiplication rule for dependent and independent events
  41. Ditferential Equations        and      Partial Derivatións
  42. LinearAlgebra :
  43. Corretation, Covariance
  44. Matrices and Vectors
  45. Addition and Scalar Multiplication
  46. Matrix Vector Multiplication
  47. Matrices Multiplication
  48. Matrix Transformations
  49. Inverse and Transpose of IVlatrices
  50. Eigen Values and Eigen Vectors
Machine Learning using Python
  1. Regression
  2. Linear Regression
  3. What is Regression
  4. Types of Regression
  5. Model Description
  6. Ordinary Lea st Squar e meth od
  7. Import and R ead the Data
  8. Perform Exploratory Data Analysis
  9. Interpreting Model Coefficients
  10. Feature Selection
  11. Training and Testing the data
  12. Model Evaluation Using Trainffest Split
  13. Training the model
  14. PredictingTestdata
  15. Model Evaluation Metrics for Regression
  16. Use Case – Linear Regression using Advertising   Dataset  and   Housing Dataset
Logistic Regression
  • Introduction
  • Data Exploration
  • Data Visualization
  • Feature Selection (Recursive Feature Elimination)
  • Implementing the Model
  • Logistic Regression Model Fitting
  • Predicting Test  Set    Results and Calculate Accuracy
  • Cross Validation
  • Confusion Matrix
  • Compute-Precision, Recall, F-Measure and support
  • ROC curve(Receiver Operating Characteristic)
  • ClassificationReport
  • Logistic Regression Hypothesis
  • Use Case – Logistic Regression using Advantages of  Random  Forest  Banking dataset
K-Nearest Neighbor
  • Understanding classification using Forest in Medicine Nearest Neighbor
  • FindK-Nearest Neighbors CLASS IFICATJON USING
  • Rescale using min-max normalization
  • Diagnosing cancer with the K-NN algorithm
  • Import /LoadData
  • Exploring and Preparing the data
  • Transformation Normalizing numeric
  • Data preparation creating training
  • Training a model on the data
  • Evaluating model performance
  • Improve model performance
Support Vector Machine (SVM)
  • Goal of Support Vector Machine (SVM)
  • Support Vector Machine-Basics
  • Advantages and   Disadvantages        of-SVMs
  • Hyperplane and Margin
  • Classification with Hyperplanes
  • Linear Separable Case
  • Kernel and Radial Functions
  • Constructing the Maximal margin classifier
  • Usecase- SVM Using cancer dataset
Decision Tree and Random Forest
  1. Understanding decision trees
  2. Calculation of Entropy and Information Gain
  3. Choosing the best split
  4. Pruning the decision tree
  5. Collection data
  6. Exploding and preparing the data
  7. Trair ing a model on the data
  8. Evaluating model performance
  9. Improving model performance
  10. Boosting the accuracy of decision trees
  11. What is a Random Forest algorithm?
  12. Advantages of Random Forest algorithm
  13. Use Case-Decision Tree and Random Forest in Medicine
  • Understanding naive Bayes
  • Basic concepts of Bayesian methods Probability
  • Joint probability
  • Conditional probability  with Bayes theorem
  • The Naive Bayes algorithm
  • The naive Bayes classific ation Using numeric features with naive Bayes
  • Naive Bayes algorithm Example
  • Collecting data
  • Exploring and preparing the data
  • Trai ing a model on the dc1ta
  • Evaluating model performance
  • Improving model performance
  • Understanding clustering
  • Clustering as a machine learning task
  • the KM-means algorithm for clustering
  • Using distance assign and update cluster :
  • Choosing the appropriate number of Custer
  • Finding segments using K-means clustering
  • Collecting data
  • Exploring and preparing the data
  • Data preparation dummy coding missing values
  • Data preparation imputing missing
  • Training a model on the data
  • Evaluating model performance
  • Improving model performance
  • Principal component analysis (PCA)
  • Dimensionality Reduction
  • Use Case – KMeans       Clustering using Wholesale Customers dataset
  • What is Dimensionality reduction?
  • Row Vector and Column Vector
  • How to represent a data set?
  • How to represent a dataset as a Matrix.
  • Data Pre-processing   Feature Normalisation
  • Mean of a data matrix
  • Data Pre-processing: Column Standardizatio n
  • Co-varian ce of a Data Matrix
  • Why learn PCA?
  • Geometric intuition of PCA
  • Mathematical objective function of PCA
  • Eigenvalues and Eigenvectors (PCA): Dimensionality reduction
  • PCA for Dimensionality Reduction and Visualization
Deep learning
  • Introduction to Deep Learning
  • Building
  • Neural network architecture
  • Convolutional Neural Networks (CNN)
Artificial Neural Networks(ANN)
  1. Deep Learning with Keras & Tensorflow
  2. Image Classification with Keras
Artificial lntelligence
  1. Natural Language Processing
  2. Introduction to NLP and NLTK
  3. Preprocessing data using tokenization
  4. Stemming text date
  5. Converting text to its base form using lemmatization
  6. Building a bag-of-words model
  7. Building a text classifier
  8. Text to Features
  9. TF-IDF Extraction
  10. Word Vectors
  11. Analyzing the sentiment of the sentence
Building Recommendation Engines
  • What is RecommendationEngine
  • Types of Recommendation Engines
  • Collaborative Filtering
  • ItemBased Collaborative Filtering
  • User Based Collaborative Filtering
  • GontentBased Filtering
Optical Character Recognition
  • Extraction of text from PDF
  • Extraction of text from the image


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