Data Science Course in Pune

About Course

Get Started on the Path to Data Science Greatness!

Data Science Fundamentals Training: Our training for data science Fundamentals is constructed for individuals lacking any prior experience in quantitative or mathematical concepts. This will by default begin with basic course concepts such as mean, median, mode, and work its way up to educate you on every element of a career in analytic or data science. This training is for you if you are a programmer, fresh graduate who wants to jump-start an exciting new career, or even a data analyst who wants to learn how to thrive in the tech industry. The best instructors lead our training, and it will give a better understanding of foundational and advanced methods currently used by industry data scientists.

Data Science and Statistics using Python, R or SAS: Enter the world of data science and statistics using the R programming language, Python or SAS. This course provides a fantastic vigorous introduction to the theoretical basis of statistical techniques and their usage in R, Python, and SAS. Don’t be terrified as this course begins with a brief Python primer, so no prior knowledge is necessary. However, if you have no programming experience or are fresh to these languages, you are likely to learn the principles promptly. Installation on Microsoft Windows-based computers is illustrated in this lesson, but code samples are interchangeable with MacOS or Linux desktop systems.

Exploration through Data Science Analytics: Navigate and analyze data interactively at high speed using Spark and Scala. Here we illustrate how RDDs (Resilient Distributed Datasets) and data frames can be advantageous for manipulating data seamlessly in this course about exploration through data science analytics.

Unraveling Machine Learning and Data Science: Use Spark's built-in features and ready-to-use libraries to easily set up complex algorithms like suggesting items with just a few lines of code.The curriculum encompasses diverse datasets and algorithms, encompassing page rank, map reduction, and graph datasets.

Real-Life Data Science Examples: All Concepts are explained with examples, case studies and R source code as appropriate. These examples cover a large number of subjects, such as A/B testing in the context of an internet company or the capital asset pricing model in scenarios from quantitative finance.

Who can Opt For a Course?

• Engineering/Management Graduates or Postgraduates aspiring to carve a career path in the data science industry
• Engineers seeking to utilize a distributed computing engine for batch or stream processing or both
• Analysts aiming to leverage Spark for the analysis of intriguing datasets
• Data scientists desiring a unified engine for data analysis, modeling, and production
• MBA graduates or business professionals eyeing heavily quantitative roles
• Engineering professionals seeking a foundational understanding of basic statistics for a career in data science
• Working professionals or recent graduates predominantly engaged in descriptive analytics, aspiring to transition into data science
• Professionals well-versed in tools like Excel, seeking proficiency in R for statistical analysis.
  
  Contact Us Now and let's Start with Your Journey For an Offline data science course in Pune 

Duration: 100 Hrs

Project: Hands on Project minimum 1 month duration.

Batch type: weekdays or weekends

Mode of Data Science Training: Classroom, Online, or Corporate Training

Trainer: Experienced Data Science Consultant

Data Sets , installations , Interview Preparations , Repeat the session until 6 months are all attractions of this particular course.

SQL Curriculum:

SQL Type:

• DDL
• DML
• TCL
• DCL
• DQL

Constraints:

• Primary Key
• Unique
• Foreign Key
• Check
• Default
• NOT Null

Clause:

• DISTINCT
• WHERE
• ORDER BY
• GROUP BY
• HAVING

Operators:

• IS NULL
• LIKE
• BETWEEN
• IN

Functions:

• String
• Date
• Numeric
• Conversion
• Aggregate

Joins:

• Inner Join
• Left Outer Join
• Right Outer Join
• Full Outer Join
• Cross Join
• Self Join

Subqueries:

• Single-row subqueries
• Multiple-row subqueries
• Correlated subqueries

Set Operator:

• Union All
• Union
• Intersect
• Minus

Views:

• Simple View
• Complex View
• Materialized views

Indexes:

• B-Tree Indexes
• Bitmap Indexes
• Function Based Indexes

Analytical Functions:

• RANK
• DENSE_RANK
• ROW_NUMBER
• FIRST_VALUE
• LAST_VALUE
• LAG
• LEAD

Advanced SQL:

• PIVOT
• UNPIVOT
• PARTITIONS

PLSQL:

• Stored Procedure
• Stored Function

Python Curriculum

Python:

• Setup Python
• "Hello world"
• Datatypes-Numbers, Strings, Boolean
• String Functions: Indexing, Slicing
• Print Formating with Strings
• Variables
• Operators
• List
• Dictionary
• Tuples
• Sets in Python
• If, Elif and Else
• For Loops
• While Loops (including break and continue, Pass)
• Functions and Recursions
• Local and Global variable
• Error and Exception Handling

Pandas And Numpy:

• Create Pandas DataFrame
• Numpy Array Operations
• Pandas DataFrame Operation

Machine Learning Curriculum

Supervised:

• MLE, MAP, Confidence Interval
• Classification Metrics
• Imbalanced Data
• Decision Trees
• Bagging
• Naive Bayes
• SVM

Unsupervised:

• Introduction to Clustering, k-Means
• K-means ++, Hierarchical
• GMM
• Anomaly/Outlier/Novelty Detection
• PCA, t-SNE
• Recommender Systems
• Time Series Analysis

Math for Machine Learning:

• Classification
• Hyperplane
• Halfspaces
• Calculus
• Optimization
• Gradient descent
• Principal Component Analysis

Neural Networks and Machine Learning:

• Introduction to Classical
  Machine Learning
• Linear Regression
• Polynomial, Bias-Variance,
  Regularisation
• Cross Validation
• Logistic Regression-2
• Perceptron and Softmax
  Classification
• Introduction to Clustering,
  K-Means
• K-means ++, Hierarchical

Deep Learning Curriculum

Neural Networks:

• MLE, MAP, Confidence Interval
• Classification Metrics
• Imbalanced Data
• Decision Trees
• Bagging
• Naive Bayes
• SVMPerceptrons
• Neural Networks
• Hidden Layers
• Tensorflow
• Keras
• Forward and Back Propagation
• Multilayer Perceptrons (MLP)
• Callbacks
• Tensorboard
• Optimization
• Hyperparameter tuning

Computer Vision:

• Convolutional Neural Nets
• Data Augmentation
• Transfer Learning
• CNN
• CNN Hyperparameters Tuning &
  BackPropagation
• CNN Visualization
• Popular CNN Architecture - Alex, VGG,
  ResNet, Inception, EfficientNet,
• MobileNet
• Object Segmentation, Localisation, and
  Detection
• Generative Models, GANs
• Attention Models
• Siamese Networks
• Advanced CV

Natural Language Processing:

• Text Processing and Representation
• Tokenization,Stemming,
  Lemmatization
• Vector space modelling, Cosine
  Similarity, Euclidean Distance
• POS tagging, Dependency parsing
• Topic Modeling, Language Modeling
• Embeddings
• Recurrent Neural Nets
• Information Extraction
• LSTM
• Attention
• Named Entity Recognition
• Transformers
• HuggingFace
• BERT

Power BI Desktop

Introduction:

• Installation
• Workflow
• Comparision of Power BI Vs Excel
  and Power BI vs Tableau etc.
• how Power BI become Popular?
• Overview
• Architecture
• What is BI?
• BI Architecture Flow

Connecting & Shaping Data:

• Connecting to source data
• shaping and transforming
• Merge Queries
• Append Queries
• date related transformations
• Column related transformations
• M Query overview
• Advanced editor
• Duplicate
• Reference
• Creating static and dynamic parameters
• Data profiling

Creating a Data Model:

• Building relational models
• Creating table relationships
• Understanding cardinality
• Exploring filter flow
  create start schema
• Snowflake Schema From Flat Schema
• Role Playing Dimension

Adding Calculated Fields with DAX :

• Understanding DAX syntax
• Adding calculated columns and Measures
• writing common formulas and functions
• DAX filter context
• Row context
• Filter context
• Date functions
• Text functions
• Interative functions
  Time intelligence functions
• Creating date table

Visualizing Data with Reports:

• Inserting charts and visuals
• customizing formats
• editing,
• conditional formatting,
• selection of right visuals as per data,
• interactions,
• applying filters and bookmarks, etc.
• Page Navigation,
• URL Configuration,
• Inserting buttons, images,
• sorting of columns,
• creating hierarchy, create groups

Row Level Security:

• Creating role as per security requirement
• Manage role
• View as role

Power BI Service

Introduction:

• Overview
• Interface
• Account tiers & user
• personas
• Interface
• Building blocks
• Creating and Managing workspaces

Administration:

• Review administration roles & types
• Admin settings & options
• Tenant settings

Connecting to Data:

• Connect data to Power BI
• Service
  Explore features
  1.Gateways
  2.Dataflows
  3.Scheduled refresh
  4.Deployment pipeline

Reports & Dashboards:

• Create reports & dashboards
• Explore Tools
  1.Data driven alerts
  2.Q&A
  3.Quick insights

Sharing & Collaboration:

• Share workspaces & collaborate with your team
• Publish apps
• Publish to web

Row Level Security:

• Test static & dynamic roles data

Premium/Pro/FreeLicense:

• Review core functionality
• Features
• Considerations
• Use cases of Power BI licenses

IntelliBI Innovations is a trusted training center in administrative and software development courses from past 7+ years. IntelliBi offers strategic preparing ways for the certification abilities to upgrade yourself in a better way. Your success is our aim. We center around offering you the best classrooms/ virtual experience alongside the best client assistance. our certification from our technology partners also extends to our facilities. Our devotion to your prosperity is reflected in our agreeable staff, eager teachers and dynamic homeroom setting and hardware.