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Course Description
Dаta Sciеnce Certification from SGIT, Steinbeis University, Germany:
Accelerate your career with Dаta Sciеnce certification from SGIT, Steinbeis University Germany , one of the leading universities in Germany. This course is a perfect blend of theory, case studies and capstone projects. The course curriculum has been designed by Steinbeis University and considered to be the best in the industry. Get noticed by recruiters across the globe with the international certification. Post certification one will gain the alumnus status in Steinbeis University.
What is the certification process?
Post completion of the training, one should take an online examination facilitated by the university and should attain 60% or more to complete the course and gain the certification. Subsequently participants can check their alumnus status on SGIT , Steinbeis Global Institute Tübingen.
Advanced Certification Program in Dаta Sciеnce and AI for Digital Transformation from IITM Pravartak:
ExcelR, in association with IITM, brings to you an add-on certification for your Dаta Sciеnce Coursе.
This certification program provides you with:
- 15+ Hours of Interactive Live-Virtual Sessions by professors of IITM.
- Optional 2-day Campus Immersion in the beautiful, state-of-the-art IITM.
- A prestigious IITM Pravartak Certificate.
What is the certification process?
During the period of your course, interactive live-virtual sessions will be conducted by professors of IITM. An optional campus immersion will also be planned, whereby a slot will be created, and you will travel to Chennai for a two-day experience at the IITM campus. Post training, you will take a short quiz on the topics discussed in the session, which will unlock your Advanced Certification in Dаta Sciеnce and AI for Digital Transformation from IITM Pravartak.
Dаta Sciеnce Coursе Training
ExcelR offers Dаta Sciеnce course, the most comprehensive Dаta Sciеnce course in the market, covering the complete Dаta Sciеnce lifecycle concepts from Dаta Collection, Dаta Extraction, Dаta Cleansing, Dаta Exploration, Dаta Transformation, Feature Engineering, Dаta Integration, Dаta Mining, building Prediction models, Dаta Visualization and deploying the solution to the customer. Skills and tools ranging from Statistical Analysis, Text Mining, Regression Modelling, Hypothesis Testing, Predictive Anаlytics, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, Tableau, Spark, Hadoop, programming languages like R programming, Python are covered extensively as part of this Dаta Sciеnce training. ExcelR is considered as the best Dаta Sciеnce training institute which offers services from training to placement as part of the Dаta Sciеnce training program with over 400+ participants placed in various multinational companies including E&Y, Panasonic, Accenture, VMWare, Infosys, etc. ExcelR imparts the best Dаta Sciеnce training and considered to be the best in the industry.
Why Should You Choose ExcelR For Dаta Sciеnce Training?
If you are serious about a career pertaining to Dаta science, then you are at the right place. ExcelR is considered to be one of the best Dаta Sciеnce training institutes. We have built careers of thousands of Dаta Sciеnce professionals in various MNCs in India and abroad. “Training to Job Placement” – is our niche. We do the necessary hand-holding until you are placed. Our expert trainers will help you with upskilling the concepts, to complete the assignments and live projects.
ExcelR has a dedicated placement cell and has partnered with 150+ corporates which will facilitate the interviews and help the participants in getting placed. ExcelR is the training delivery partner in the space of Dаta Sciеnce for 5 universities and 40+ premier educational institutions like IIM, BITS Pilani, Woxen School of Business, University of Malaysia, etc. Faculty is our strength. All of our trainers are working as Dаta Scientists with over 15+ years of professional experience. Majority of our trainers are alumni of IIT, ISB and IIM and a few of them are PhD professionals. Owing to our faculty, ExcelR’s certification is considered to be the best Dаta Sciеnce certification offered in this space. ExcelR offers a blended learning model where participants can avail themselves classroom, instructor-led online sessions and e-learning (recorded sessions) with a single enrollment. A combination of these three modes of learning will produce a synergistic impact on learning. One can attend an unlimited number of instructor-led online sessions from different trainers for 1 year at no additional cost. No wonder ExcelR is regarded as the best Dаta Sciеnce training institute to master Dаta Sciеnce concepts and crack a job.
What Is Dаta Sciеnce? Who Is Dаta Scientist?
Dаta Sciеnce is all about mining hidden insights of dаta pertaining to trends, behaviour, interpretation and inferences to enable informed decisions to support the business. The professionals who perform these activities are said to be a Dаta Scientist / Sciеnce professional. Dаta Sciеnce is the most high-in-demand profession and as per Harvard and the most sort after profession in the world.
Why One Should Take The Dаta Sciеnce Coursе?
Is Dаta Sciеnce certification being worth pursuing as a career?
The answer is a big YES for myriad reasons. Digitalization across the domains is creating tons of dаta and the demand for the Dаta Sciеnce professionals who can evaluate and extract meaningful insights is increasing and creating millions of jobs in the space of Dаta Sciеnce. There is a huge void between the demand and supply and thereby creating ample job opportunities and salaries. Dаta Scientists are considered to be the highest in the job market. Dаta Scientist career path is long-lasting and rewarding as the dаta generation is increasing by leaps and bounds and the need for the Dаta Sciеnce professionals will increase perpetually.
- 1.4 Lakh jobs are vacant in Dаta Sciеnce, Artificial Intelligence and Big Dаta roles according to NASSCOM
- The world will notice a deficit of 2.3 Lakh Dаta Sciеnce professionals by 2021
- The Demand for Dаta Scientist professionals has increased by 417% in the year 2018, in India, as per the Talent Supply Index
- Dаta Sciеnce is the best job to pursue according to Glassdoor 2018 rankings
- Harvard Business Review stated that ‘Data Scientist is the sexiest job of the 21st century’
You May Question If Dаta Sciеnce Certification Is Worth It?
The answer is yes. Dаta Sciеnce / Anаlytics creating myriad jobs in all the domains across the globe. Business organizations realised the value of analysing the historical dаta in order to make informed decisions and improve their business. Digitalization in all the walks of the business is helping them to generate the dаta and enabling the analysis of the dаta. This is helping to create myriad dаta science/analytics job opportunities in this space. The void between the demand and supply for the Dаta Scientists is huge and hence the salaries pertaining to Dаta Sciеnce are sky high and considered to be the best in the industry. Dаta Scientist career path is long and lucrative as the generation of online dаta is perpetual and growing in the future.
Why ExcelR Is The Best Dаta Sciеnce Training Institute?
ExcelR offers the best Dаta Sciеnce certification online training along with classroom and self-paced e-learning certification courses. The complete Dаta Sciеnce course details can be found in our course agenda on this page.
Who Should Do The Dаta Sciеnce Coursе?
Professionals who can consider Dаta Sciеnce course as a next logical move to enhance in their careers include:
- Professional from any domain who has logical, mathematical and analytical skills
- Professionals working on Business intelligence, Dаta Warehousing and reporting tools
- Statisticians, Economists, Mathematicians
- Software programmers
- Business analysts
- Six Sigma consultants
- Fresher from any stream with good Analytical and logical skills
Interview Preparation Sessions
Participants who have completed the Dаta Sciеnce course training and the projects will be put under our Placement Incubation Program. As part of this program, participants will undergo a thorough interview preparation process on Dаta Sciеnce. A huge repository of Dаta Sciеnce Interview questions with answers will be provided for the participants to prepare. A dedicated Dаta Sciеnce Subject Matter Expert (SME) will help in resume building, conduct mock interviews and evaluate each participant's knowledge, expertise and provide feedback. Our SMEs will do the necessary handholding on interview preparation process till the time the participant is placed. Guidance is also provided on Linkedin profile building and tricks of the trade to improve the marketability of the resume. - ExcelR Management
Projects
- As more and more people are expressing their views and opinions on various microblogging websites about various products and services. There has been a surge of dаta generated by the users, these websites have people sharing their thoughts daily.
- Sentiment Analysis with the help of Natural Language Processing technique for identifying the sentiments of a product or service
- Customers are looking for more information before buying a product on E-commerce websites. Amazon introduced a new feature 'question and answer' search field for products.
- The project is to build an information retrieval system from Amazon products dаta based on NLP techniques. Top 5 relevant answers to be retrieved based on input question
- Reducing the risk of fraudulent loans by carefully evaluating the risk & at the same time increasing profits by rejecting only those loans, which have the potential of defaulting
- The objective of the analysis to predict an item when sold, what is the probability that customer would file for warranty and to understand important factors associated with them
- Predict which flights would be delayed and by how long?
- Flight delays cost the industry an estimated $25 billion every year More than 60 percent of frequent flyers cite delays among the things about air travel that they find most dismaying. And the costs are spread around - an extra $25 in parking here, a missed business meeting there. Carriers, meanwhile, pay an estimated $62 per minute in crew, fuel, maintenance and other costs. It adds up.
Career Progression and Salary Trends
Learning Path
Course Curriculum
Data Science
- What is Data Science? Use cases with Business Problem (Mobile/Banking) and How ML gives a solution, Types of Roles, what learnings are important, VAC courses offers, Jumbo Pass, Q & A.
- ML Project Life Cycle(Problem, Collecting the data, EDA,Cleaning,Transformation, Partition, Model fitting, Cross validation, Metrics, Deployment),
- Sample, population, Data types(continous, discrete), Central tendency, spread, shape of the data such histogram, skewness, kurtosis
- Bargraph, Box plot(IQR, Whisker lengths, outliers), Scatter plot( Positive , Negative, Neutral), correlation
- Intro to Python language,Anaconda Installation(Jupyter, Spyder), Datatypes(Int, Float,dic,Set), operators(Arthemetic,comparision,Logical, Assignment)
- Data structures(List (types of list methods such as append ,extend ,insert ,remove ,pop ,clear ,index ,count ,sort ,reverse), tuples,dictionary,set), What are Control structures (if, ifelse, if elif, Nested if)
- For loop, functions, numpy(scalar,array, vector, 1 dim, 2 dim, random int), converting numpy to pandas, giving column names, Importing pandas, (read_csv, head, tail, describe)
- Pandas (info, selecting columns, dropping columns, groupby, concat(row and columns),merge, removing duplicates, filling blanks with mean)
- EDA (showing graphs such as histogram, boxplot, bargraph, scatter plot, heat map using matplotlib, seaborn) using Google collab with generative AI usage. Giving an example dataset ask them to work in class
- Probability, Normal distribution theory, standardization, zscore, z tables, applications, python code, confidence Interval
- Level of significance, Hypothesis Testing (One sample Z test, Two sample Z test), t-test
- Simple Linear Regression, metrics such RMSE and R square - Working on Age vs Weight example
- Intro to Regression models , MLR - Assumptions of Linear Regression, Variable selection, Multicollinearity VIF
- what is meant by classification models ? When do we choose Logistic regression, modelfitting, confusion matrix, accuracy score - Working on Breast cancer case study
- Other metrics Sensitivity, Specificity, precision , F1 score, ROC curve, AUC score
- Data Transformation(Standardard scaler, minmax scaler, label encoding, one hot encoding) and Data partition (Training and Test)
- Cross validation (Stratified K-Fold, K-Fold cross validation,Shuffle Split Cross-Validation)
- Variance Biased Trade-off(under fitting-causes-Lack of training , best fit, over fitting - causes -Noise in training data,Too many training epochs or iterations, too many variables) ,Visualizations (Underfitting ,bestfit, Overfitting) and Feature Engineering - Working on Bangalore housing prices case study
- Techniques such Lasso, Ridge, ElasticNet - Working on "Banglore housing prices" case study.
- Support vector machine (Hyperplane, Maximum margin classifier, Support Vectors, SVM for Linear Classification , SVM for Non-Linear Classification(polynomial, RBF, Sigmoid)
- Decision Tree Structure(Root node,Internal nodes,terminal nodes),Gini Impurity, Entropy and Information Gain (for classification), Overfitting and Underfitting in Decision Trees, Pruning,Hyperparameters - Working on Sales data set using python
- Ensemble Methods: Bagging and Random forests , working on hyper parameters to control overfitting.
- Sequential methods: Gradient Boosting, Ada Boost, using Grid search CV
- XG Boost, LightGBM
- Final project with Deployment
- What are DImensional Reduction Techniques ? 1. Purpose of PCA 2. Eigenvectors/Eigen values 3. Applications 4. Advantages 5. Working on case study
- Introduction to Clustering, Distance Metrics,Clustering Algorithms(K mean, dbscan),Choosing the Right Number of Clusters(Elbow Method,Silhouette Analysis)
- what is Recommendation and why it is important? What is Collaborative Filtering (CF) And Content-Based Filtering ?
- Time series Concepts, components, Visualization,Data partition, Lagplot, ARIMA models,Python code on ARIMA models
- Perceptron , Single Layer Network, activation functions, Back propagation method, Simple ANN code
- Multilayer Neural network, Gradient Descent method, optimizers, learning rate - complete code with tensorflow
- RNN - use cases, vanishing and exploiding problem, Simple RNN code
- LSTM Architecture, Working model, LSTM vs GRU, python code
- What is Text Data,Various forms,Applications, Tex pre-processing(Tokenization,Normalization,Stopwords,Lemmatization,stemming), Visualization on preprocessed text data
- Text Representation: Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Sentiment Analysis, Classification model using ML
- Named Entity Recognition (NER), What is Word Embedding?
- What are pre-trained word Embeddings, Word2Vec(Skip gram, CBOW), real time applications, example codes
- Language Modeling: N-gram Models, Neural Language Models, applicaton of RNNs, LSTMs on Text data
- Large Language Models? Transfer Learnings in NLP, what are pre-trained models?
- what are tansformers? Hugging Face transformers library and its use cases
Core Python
- Python Introduction - Programing Cycle of Python, Python Installation, Python IDE Variables , Data types
- Operator -Arthmatic ,Comparison , Assignment ,Logical , Bitwise opeartor, List, Tuple, Set, Dictironary
- Conditional Statements (if, if-else, if elif, Nested if), Loops in Python (for, while), Loop Control Statements(break, continue, pass)
- Function - Define function , Calling function, pass by refernece as value , Function arguments , Anonymous functions , return statements Scope of variables - local & global,Lambda, map, filter, reduce
- Importing modules, Creating user-defined modules, Python Standard Library,Installing packages using pip
- Importing the data,Handling Missing Data: ,Filtering Out Missing Data ,Filling In Missing Data ,Data Transformation ,Removing Duplicates
- Data Type Conversion, Detecting outliers using Boxplot, Z score, Handling Outliers (Capping,Transformation,Removal),
- Transforming Data Using a Function or Mapping ,Replacing Values , Feature Engineering such as Creating new variables ,Aggregations and groupings
- Hierarchical Indexing,Combining and Merging multiple datasets (merge(), join(), concat()),Reshaping and Pivoting
- Convert to datetime ,Extract attributes ,Create datetime range ,Resample data ,Time delta calculations ,Add time offset ,Time zone conversion ,Set datetime index ,Filter by date ,Handle missing time data
- 1. Exception Handling : Try, except, else, finally ,Built-in exceptions ,Raising exceptions ,Custom exceptions ,Hands-on error handling tasks
- 2. Regular expressions: match function , search function , matching vs searching Regular exp modifiers and patterns
- Class and Object, __init__ method , Attributes and methods, Hands-on: Create simple classes
- Inheritance,Polymorphism,Hands-on: Real-world OOP examples
- Encapsulation and Abstraction,Hands-on: Real-world OOP examples
- Iterators and Generators, Decorators
Tableau
- What is Tableau ?
- What is Data Visulaization ?
- Tableau Products
- Tableau Desktop Variations
- Tableau File Extensions
- Data Types, Dimensions, Measures, Aggregation concept
- Tableau Desktop Installation
- Data Source Overview
- Live Vs Extract
- Overview of worksheet sections
- Shelves
- Bar Chart, Stacked Bar Chart
- Discrete & Continuous Line Charts
- Symbol Map & Filled Map
- Text Table, Highlight Table
- Formatting: Remove grid lines, hiding the axes, conversion of numbers to thousands, millions, Shading, Row divider, Column divider Marks Card
- What are Filters ?
- Types of Filters
- Extract, Data Source, Context, Dimension, Measure, Quick Filters
- Order of operation of filters
- Cascading
- Apply to Worksheets
- Need for calculations
- Types: Basic, LOD's, Table
- Examples of Basic Calculations: Aggregate functions, Logical functions, String functions, Tablea calculation functions, numerical functions, Date functions
- LOD's: Examples
- Table Calculations: Examples
- What is Data Combining Techniques ?
- Types
- Joins, Relationships, Blending & Union
- Dual Axis
- Combined Axis
- Donut Chart
- Lollipop Chart
- KPI Cards (Simple)
- KPI Cards (With Shape)
- What are Groups ? Purpose
- What are Bins ? Purpose
- What are Hierarchies ? Purpose
- What are Sets ? Purpose
- What are Parameters ? Purpose and examples
- Reference Lines
- Trend Line
- Overview of Dashboard: Tiled Vs Floating
- All Objects overview, Layout overview
- Dashboard creation with formatting
- Actions: Filter, Highlight, URL, Sheet, Parameter, Set
- How to save the workbook to Tableau Public website ?
Mysql
- Introduction to Databases, Introduction to RDBMS, Explain RDBMS through normalization, Different types of RDBMS , Software Installation(MySQL Workbench)
- Types of SQL Commands (DDL,DML,DQL,DCL,TCL) and their applications Data Types in SQL (Numeric, Char, Datetime)
- SELECT:LIMIT,DISTINCT,WHERE AND,OR,IN, NOT IN,BETWEEN, EXIST, ISNULL ,IS NOT NULL,Wild Cards, ORDER BY
- Usage of Case When then to solve logical problems and handling NULL Values (IFNULL, COALESCE)
- Group By, Having Clause. COUNT, SUM,AVG,MIN, MAX, COUNT String Functions, Date & Time Function
- NOT NULL, UNIQUE, CHECK, DEFAULT, ENUM, Primary key,Foreign Key (Both at column level and table level)
- Inner, Left, Right, Cross, Self Joins, Full outer join
- DDL: Create, Drop, Alter, Rename, Truncate, Modify, Comment
- DML: Insert, Update & Delete TCL: Commit, Rollback, Savepoint and Data Partitioning
- Indexes (Different Type of Indexes) and Views in SQL
- Stored Procedures - Procedure with IN Parameter, Procedure with OUT parameter, Procedure with INOUT parameter
- User Define Function, Window Functions - Rank, Dense Rank, Lead, Lag, Row_number
- Union, Union all,Intersect, Sub Queries, Multiple Query
- Handling Exceptions in a query, CONTINUE Handler, EXIT handler, Loops: Simple, Repeat, While Cursor
- Triggers - Before | After DML Statement
MLOps
- What is MLOps, Different stages in MLOps, ML project lifecycle, Job Roles in MLOps
- What is Development stage of an ML workflow , Pipelines and steps, Artifacts, Materializers, Parameters & Settings
- Stacks & components, Orchestrators, Artifact stores, Flavors etc.
- ML Server infrastructure, Server deployment , Metadata tracking
- Collaborations, Dashboards
ChatGPT
- History and Development of ChatGPT,Examples of ChatGPT use in various industries, Basics of Transformers, Key concepts and principles of Generative AI,Examples of Generative AI models including ChatGPT, open source LLM's, Prompting basics, Overview of Different ChatGPT models
- Prompt Techniques, Few-shot Prompting, Zero Shot prompting, One-Shot Prompting, Chain of Thought Prompting ChatGPT applications in everyday life such as writing,translation and creativity, Explore ChatGPT potential for Education , Work, and Business Use Cases
- Code generation, code explaination, machine translation, structured and unstructred outputs, Canvas, deep research, image generation , video generation, codex, plugins, Browsing
- Utilizing ChatGPT for Excel, word, powerpoint, web development,data anlaysis, programing, Dashboards ,ChatGPTprojects etc.
- Seeking jobs,career changes, working on resume, and updation, networking, job search strategies using ChatGPT, Linkedin Profile Optimization
- Introduction to OpenAI API & usage limits
- Authentication, Endpoint usage
- Integrating GPT with Python, Google Sheets, Excel, Power BI Zapier, Make, LangChain basics
Artifial
- 1. Linear Algebra: Vectors, matrices, dot product, matrix multiplication
- 2. Calculus: Derivatives, partial derivatives, chain rule (for backpropagation)
- 3. Review on ANN
- 1. What is Gradient Descent in detail? Connectivity of Calculus in Back propagation. Weight & Bias Updates,
- 2. Types of Loss functions(MSE, Binary Cross entropy (Binary & Muliti)
- 3. Overfitting Solutions(Dropout, Early stopping)
- 4.Types of Optimizers and acitvations functions its applications
- 5. Example Case study
- 1. CNN,DeepConvolutionModel,DetectionAlgorithm, CNN FaceRecognition
- 2. Working on MNIST data set
- 1. Introduction to Web Scraping & Web Basics,
- 2. Python Libraries for Web Scraping (requests, BeautifulSoup)
- 3. HTML & Web Page Structure Basics
-
Selecting a website and extract the data
- 1. To Extract Image, Reviews, Ratings, and Price Tags
- 2. Store in Structured Format
- 3.
- a) Image classfication from images
- b) Sentiment analysis from Reviews
- c) Regression model from Prices.
Big Data
- What is big data, characteristics of big data, technologies in big data etc.
- what is spark environment, spark documentation, installation of spark , spark concepts
- Integration with different languages like python , r, scala, etc. Introducing pyspark environment , pyspark basics and functions
- Pyspark RDD structures, dataframe modules, sql modules , examples , exercise problems, working on datasets
- Pyspak ML libraries, Regression models, linear and logistic regression and clustering basics, tree based models, ensemble concepts
- Pyspark ML applications, with excercises, visualizations
- What is databricks, account creation, cluster creation, working on pyspark applications in databricks with r, python and scala
- What is aws cloud, account creation , understanding basic aws enevironment and knowledge
- What is hadoop , hadoop architecture, creating hadoop environment on AWS cloud, install java, install hadoop and related concepts
- Running applications like map reduce on data , getting insights , doing analysis, word count problems etc.
Azure
- What is cloud computing, why it is important, cloud services, applications, benefits , architectures
- What is Azure, Why Azure, Azure services, Azure core architecture, core azure services domains, creation of azure account
- Intro to AI/ML services, What is azure ml designer studio, developing ml models, python and r applications in studio
- Resource groups, virtual machine concepts , storage service, web apps, databricks environment , azure sql databases, billing etc.
- What is azure open ai, open ai documentation, how to use azure open ai studio, creating applications, different models in azure open ai
Basic Of R
- Data types(Numeric, Char, logical, Complex, Vector, List, Matrix, Factor, Array, Data frame), Relational Operators, Logical Operators
- If, Ifesle, For loop, While loop, Repeat, Functions
- Merging data frames, Analyzing Iris Dataset using apply functions, dplyr package(Filter, Set, Arrange), Data Visuzlization using ggplot2, Scatterplot, Histogram, Boxplot
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