Features
- Lectures - 70
- Duration - 35 Weeks
- Case Studies and Assessment - 15+
- Delivery Mode - Online/Offline
- Batches - Weekdays/Weekend
- Capstone Projects - 20+ projects( Choose anyone)
Data Science is a cross disciplinary blend of tools and technologies which work conjointly to understand business, clients, patterns and resolving inquisitions which we yet not perceived from the data stored from data warehouses and all possible web applications. This program is the perfect blend of Statistics, Programming, Machine learning, Deep Learning, Artificial Intelligence, Data Visualization and big data designed to give you a holistic view of Data Science.
Program Overview
- The most comprehensive curriculum with training material designed by NASSCOM, along with its 35 SIG(Special Interest Group) members such as Goldman Sachs, IBM, Ins Analytics, Infosys BPO,Insights of Data, JP Morgan, Karvy Analytics,Knod Global,KPMG,Wipro,WNS,Wells Fargo,Amazon,Capgemini,Concentrix,CITI,Cyient Insights,Accenture,EXL,First America,Fractal Analytics,GENPACT,Google,ADP Deloitte,HCL,HDFC,IBM,ISC2,NIIT University,PwC,Symantec,TCS to name a few,that will prepare you for future externalities in the data analytics industry and fulfil the gap of academics and industry requirements.
This official NASSCOM-CERTIFIED Data Science Program covers R,Python training, Data Wrangling, Interaction with Big Data Hadoop, MapReduce, Sqoop, Flume, Hive, Pig, HBase, Spark, R with Statistics, Data Mining, Machine Learning Algorithms, Deep Learning, Natural Language Processing, Computer Vision, Reinforcement Learning, Time-Series forecasting, SQL, Tableau and SAS.
Program Structure
- 50 hour Pre-Learning: Before you come in, get ready for the Program.You will get a series of online recorded tutorials to understand the structure of Data Science.
- 197 hours Program: Here, you will get Hands-on Experience on Advance Excel, SQL,R Programming, Python Programming, Statistics, Machine Learning, Artificial Intelligence, Big Data and Tableau.
- 353 hours Post Program:Learning does not stop here. After completing the Program, you will work on Project, Assignments. Doubt clearing is also provided. You will be working on any one capstone project from the list of few projects on your choice.
- 130 Hours of Electives:Grab an opportunity to add the advanced knowledge on data science, artificial intelligence, big data, java, non relational data bases, business intelligence tool, natural language processing, object detection to the existing pool of knowledge by opting the electives. Here you will be working on advance concepts of statistics, machine learning algorithms, SQL and business intelligence tools like Tableau and Power BI
Price:
- Elective 1: Advanced Data Science and Neural Network , INR 24999+GST, $407
- Elective 2: Advanced Bigdata Analytics, INR 21,999+GST, $365
- Elective 3: Advanced Business Intelligence, INR 11,999+GST, $220
- Elective 4: Advanced Artificial Intelligence with Computer Vision & Natural Language Processing, INR 11,999+GST, $220
Eligibility:
- Work Exp –Working professionals in IT / Analytics / Statistics / Big Data / Machine Learning.
- Education – Fresh Graduates from Engineering/ Mathematics / IT backgrounds.
Sample Certificate
Nasscom Certificate –
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Advance Excel
Microsoft Excel Overview, Formatting excel shortcuts Basic Formulas, sorting data, filtering data, column chart Pie chart, Pivot tables, Vlookup, Match Function, Lookup Function, VBA
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SQL
BASIC OF DATABASE, TYPES OF DATA BASE, DATA TYPES, SQL OPERATORS, EXPRESSION (BOOLEAN, DATE, NUMERIC), CREATE, INSERT, DROP, TRUNCATE, DELETE, ALTER, UPDATE, SELECT, RANGE OPERATER, IN, WILDCARD, LIKE CLAUSE, CONSTRAINT, AGGREGATION FUNCTION, GROUP BY, ORDER BY, HAVING, JOINS, CASE, COMPLEX QUERIES,
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R Programming
What is R Programming, Variables and Data Type in R, Logical Operators,Vectors,List,Matrix,Data Frame,Flow Control, Functions in R, Data Manipulation in R- dplyr, Data Manipulation in R- tidyr, Data Visualization In R
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Advance Statistics
Introduction to Statistics, Descriptive Statis, Population and Sample, Types of Data, Percentile, Quartile, IQR, Corelation and Covariance, Measure of Central Tendency, asymmetry and variability , Skewness, Kurtosis, Central Limit Theorem, Confidence Interval, Hypothesis Testing, p-value, T-test, Z-test, F-test
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Python Programming
Introduction to Python, What and Why Python, Variables, Databases and operation, Datatypes and operation , Operators, Block Structure, Data Structures, Functions , Modules, Class, Numpy, Pandas, Matplotlib, Seaborn
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Machine Learning and Deep Learning
Introduction to Machine Learning, Types of Machine Learning, Linear Regression, Logistic Regression, Decision Trees, Naive Bayes, K-Nearest Neighbor, Support Vector Machine, Random Forest, PCA, K-Means, Introduction to Neural Network, Foreward Propagation, Activation Function, Optimizers, Padding, Pooling, Convolution, Check Points, Neural Network Implementation, Time Series Analysis
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Artificial Intelligence [Natural Language Processing , Computer Vision , Reinforcement Learning
✔️ Computer Vision [10Hrs]
Introduction to Image Processing, Feature Detection, OpenCV, Convolution, Padding, Pooling & its Mechanisms, Forward Propagation & Backward Propagation for CNN CNN Architectures AlexNet, VGGNet, InseptionNet, ResNet, Transfer Learning
✔️ Natural Language Processing [12Hrs]
Introduction to text mining, text processing using python, Introduction to NLTK, Tokenization, Stemming, Bag of words, Sentiment Analysis, Name- Entity Recognition, Text Segmentation, Text Mining, Text Classification
✔️ Reinforcement Learning [5 Hrs.]
RL Framework, Component of RL Framework, Examples of Systems, Types of RL Systems, Q-Learning -
Big Data Analytics with Hadoop and Spark
✔️ Hadoop
Introduction to Hadoop, Scaling (Horizontal and Vertical), Challenges in Scaling, Concept and challenges in parallel computing, Distributed Computing and use in Hadoop, Core components of Hadoop, Hadoop working Principle, Hadoop Commands and implementation
✔️ MapReduce
Mapreduce, Mapreduce Implementation, Mapreduce Implementation
✔️ Pig
Introduction to pig, Installation of PIG, PIG Query
✔️ Hive
Introduction to Hive, Hive Installation, Hive Implementation, HIVE_SQL Opeartions
✔️ HBase
Introduction to Hbase, Installation of Hbase, Hbase Query
✔️ Sqoop
Introduction to Sqoop , Installation of Sqoop
✔️ Flume
Introduction to Flume, Installation of Flume, Flume Queries
✔️ Oozie
Introduction to Oozie, Installation of Oozie, Oozie Query
✔️ Spark
Introduction to Spark, Resilient Distributed Datasets (RDDs), Spark components
✔️ PySpark
Introduction of Pyspark, Installation of Pyspark, Queries -
Tablaeu
TABLEAU PRODUCTS, INSTALLATION OF TABLEAU DESKTOP/PUBLIC, CONNECTING TO DATABASES, REPLACING DATASOURCE, TABLEAU CANVAS INTERFACE, DATA TYPES, DRILL DOWN, HIREARCHIES, MEASURES, DIMESIONS, SORTING, GROUPING, PARAMETER, SETS, COMBINE, DATA BLENDING, FILTER, MARKS CARD, DUAL AXIS, CALCULATED FIELD, VISUALZATION, CANVAS FORMATING, DASHBOARD CREATION
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PowerBI
POWER BI PLATFORM, PROCESS FLOW, FEATURES, DATASET, BINS, PIVOTING, QUERY GROUP, DAX FUNCTION, FORMULA, CHARTS, REPORTS, DASHBOARDS
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SAS
Introduction to SAS, Data Sets, Libraries, Importing Data , SAS Value Manipulation, Exporting Data, Processing Observation & Variables Sorting, Conditional Execution, Assignment Statement, Modifying Variable attribute, Functions, Coercion, Loops, Data Validation, Data Wrangling Generate Report, Summary Reports, Frequency Tables, Report Enhancement User defined formats, Titles, Footnotes, SAS system reporting, OD statement Logic error, Syntax Error, Data Error,
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Elective 1: Advanced Machine Learning and Neural Networks [60 Hours]
- ✔️ Probability
- ✔️ Probability distribution -> Discrete Probability Distribution (Probability Mass Function, Binomial Distribution, Poisson Distribution, Discrete Uniform Distribution) Continuous Probability Distribution (Probability Density Function, Normal Distribution, Continuous Uniform, Exponential Distribution)
- ✔️ Hypothesis Testing (Z Distribution, Student’s T distribution [ One Sample T-test, Two Sample T-test], Chi Square [Chi-Square goodness of fit, Chi square test of independence])
- ✔️ Bayes Theorem
- ✔️ Correlation [Pearson Corelation, Linear Corelation]
- ✔️ Cross Validation [ K-fold cross-validation, Hold-out cross-validation, Stratified k-fold cross-validation]
- ✔️ Hyperparameter Tuning
- ✔️ Ensemble Learning [Lasso Regression, Ridge Regression, Adaboost, Adagrad]
- ✔️ Time Series Analysis (Introduction to TSA, Component of TSA, Methods to check stationarity [ ADF, KPSS] , AR, Moving Average [SMA, CMA, EMA], ACF, PACF, ARMA, ARIMA, LSTM, GRU)
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Deep Learning
- ✔️ Optimizers [SGD, RMSProp, Adam, Adadelta, Adamax, Adagrad, Adam]
- ✔️ Activation Function [Linear , Sigmoid, Tanh, ReLu, Leaky ReLu, ELU, Softmax, Swish]
- ✔️ Loss -> Classification (BinaryCrossentropy class, CategoricalCrossentropy class, SparseCategoricalCrossentropy class, Poisson class, binary_crossentropy function, categorical_crossentropy function, sparse_categorical_crossentropy function) Regression (MeanSquaredError class, MeanAbsoluteError class, MeanAbsolutePercentageError class, MeanSquaredLogarithmicError class, CosineSimilarity class)
- ✔️ Weight Initialization (Xavier Weight Initialization, Normalized Xavier Weight Initialization, He weight Initialization)
- ✔️ Bias Initialization
- ✔️ Callbacks (ReduceLROnPlateau, ModelCheckpoint, Earlystopping, TensorBoard)
- ✔️ Keras Tuner
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Elective 2: Advanced Bigdata Analytics [33 Hours]
- ✔️ Scala
- ✔️ No SQL (Introduction to MongoDB, Installation, Data Modelling, Database operation- create and drop, Collection- create and drop Data types, Insert, Query, Update, Delete, Find, Limit , Skip, Create Index, ObjectID, Aggregate, Replication, Shrading, Dumping and Restore, Mongostat and Mongotop, MongoDB client setup using Java, Reference and Embedded Relationship, DbRefs, Cover Query $explain, $hint, findAndModify, Indexing Array and subfield, Mapreduce, Text Search, Index, RegeX)
- ✔️ Java (Installation, Syntax main()/printIn()/print()/, Variable [String, Int, Boolean, float, char], Datatypes, Operators, conditions, loop Methods, class, file handling)
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Elective 3: Advanced Business Intelligence [18 Hours]
- ✔️ SQL (Trigger, Stored Procedures, Common Table Expression, Index, Except, Exists, Grouping set, Pivot, Rollup, Cube, Constraints, Partition)
- ✔️ Tableau (Pareto Analysis, Table calculation, Multiple data source blending, Advanced charting techniques, Tableau prep tool)
- ✔️ PowerBI (Multiple data source blending, Basic of SSIS for ETL)
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Elective 4: Advanced Artificial Intelligence using Computer Vision and Natural Language Processing [18 Hours]
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Program Benefits
- ✔️ Cutting Edge Curriculum: Hand crafted Course content made by Experts from various Industries. Learn through Practical case studies and multiple projects.
- ✔️ On the Go Learning: Online accessible E-learning Material, recorded lectures, case studies and Research Paper through our system.
- ✔️ Build Solid Foundation: 197 hours focused course on Data Science.
- ✔️ Industry Mentorship: Get 1 to 1 guidance from Industry experts and start your career in Data Science.
- ✔️ Earn a Government of India approved & globally recognized certificate by NASSCOM IT- ITes SSC by clearing NASSCOM assessment examination.
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Skills you will possess post program
- ✔️ Data Manipulation
- ✔️ Data Wrangling
- ✔️ Data Cleaning
- ✔️ Data Visualization
- ✔️ Big Data Architecture/Engineering
- ✔️ Data Analysis
- ✔️ Descriptive Analytics
- ✔️ Machine learning Modelling
- ✔️ Predictive Analytics
- ✔️ Text Processing
- ✔️ Image Processing
- ✔️ Sentiment Analysis
- Video Analytics
- Emotion Analysis
- Face Recognition/Detection
- Optical Character Recognition
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Capstone Projects
- The most effective way to learn Data Science is to learn practically. Once the program gets finished candidates will be provided with a few Projects based on Machine Learning. You are advised to choose any 1 project according to your domain and your interest. Some examples of capstone project:
- ✔️ Prediction of Future security prices
- ✔️ Credit card Risk Analytics
- ✔️ Sales Prediction for Big Mart
- ✔️ Project for a real estate company that wants to Predict the prices of houses based on different parameters.
- ✔️ Food Demand Forecasting
- ✔️ Black Friday Sales Prediction
- ✔️ Image Recognition Using Computer Vision
- ✔️ Social Media Analysis(Sentiment Analysis)
- ✔️ Recommendation System
- ✔️ Image Classification Using Computer Vision
