Best Data Science Course in Mumbai - Online Training Classes Available
Learners 1000+
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Best Data Science Course in Mumbai to Learn from Best Data Science Training Institute and Classes in Online / Classroom Format in Reasonable Fees with Placement Support from Experts!
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About Data Science -
It is Umbrella term that covers everything like analysing data, data cleaning, data visualization etc. As name suggest it is science of analyzing data to extract meaningful information from it to help management take decisions for company. Machine Learning and Adv. Machine Learning or Aritificial Intelligence (A.I.) provides various Data Analysis Algorithms to solve Data Analysis projects. Finally data is represented graphically or summary data using visualization tools like Tableau, Power BI etc. Hence Data analytics or Data Visualzization comes in combination or required along with Data Science. Combination of both makes you a Data Scientist!
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About Our Data Science & Analytics Certification Course with Real Time Projects -
Learn from basics of Python to advanced A.I. and Analytics modules on Real Time Projects (watch video below) under or Experts. Can be learnt by both freshers and experienced to become a Data Science Professional
Duration & Training Packages -
Data Science & A.I. - 3.5 to 4 months - Get Fees
Data Science, A.I. & Analytics - 6 months - Get Fees
customized training available.Trainer -
Exp. Professional, Trained Lots of Students!
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Jobs Roles Targeted -
Data Scientist, Machine Learning Engineer, Data Analyst etc.
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Any Pre-requisites -
None. Any one from IT or non IT can learn.
Upcoming Batch -
New batch at regular intervals. Upcoming Batch
Syllabus
Syllabus
Data Science & Analalytics Course
- Introduction & Installation
- Introdution to Python & Python Analytics or Adv. Python
- Python / Anaconda Installations & Configuration
- Programming Basics
- Variables
- Data Types
- User Inputs
- Unpacking
- Boolean Practice
- What If, Else and If, Making Decisions
- Loops
- Functions
- Creating Functions
- Using Built in Functions
- OOPs Programming
- Modules
- Classes & Objects
- Inheritance
- Overloading
- Composition
- Data Structures
- List
- Tuple
- Set
- Exception Handling
- MySQL Server Setup
- Installing and Configuring MySQL Server
- Creating Database
- CRUD Operations
- Create Tables
- Insert
- Update
- Delete
- Database Integration with Python
- Connection to Database from Python
- Writing functions to handle database operations
- CRUD Operations in python
- Web Scrapping
- Getting Data from Any Website using Python (new)
- Data Analytics in Python
- Installing & Understanding Analytics Packages
- NumPy Array Operations
- Pandas Data Frame Operations
- Data Acquisition (Import & Export)
- Indexing
- Selection
- Sorting
- Filtering
- Group By
- Binning
- Concatenation
- Merge
- Append
- Drop
- Data Visualization in Python
- Installing Librarires
- Matplotlib
- Seaborn
- Charts & Plots
- Histogram
- Scatter Plots
- Box Plots
- Line Chart
- Bar Chart
- Pie Chart etc.
- Dashboards
- Project
- Installing Librarires
- Beautiful SOAP library
- Project
- Machine Learning Examples
- Introduction to Machine Learning & Data Analysis
- Machine Learning Examples
- Statistics & Exploratory Data Analysis (EDA)
- Descriptive Statistics
- Descriptive Analytics
- Measure of Central Tendency
- Dispersion
- Probability Distribution
- Correlation & Covariance
- Inferential Statistics
- Diagnostic Analytics
- Outliers | Missing Values
- Sparsity
- Inferential Statistics
- Random Sampling
- Hypothesis Testing
- Supervised Learning
- Installing & Using Scikit-Learn Package
- Linear Regression
- Multi-Linear Regression
- Decision Tree (CART)
- Ensemble Learning
- Random Forest
- xgBoost
- K Nearest Neighbors (KNN)
- Support Vector Machine (SVM)
- Kernel Functions
- Naive Bayes Classifier (NBC) / MultinomialNB Algorithm
- GRID SEARCH CV AND RANDOM SEARCG CV
- Linear Discriminant Analysis (LDA)
- Unsupervised Learning
- Hierachical Clustering
- K Means Clustering
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- Time Series Forecasting
- Time Series Data
- Trend Chart
- Stationarity
- Seasonality
- Moving Average
- Exponential Smoothing
- ARIMA
- GARCH
- Model Improvement & Validation
- Regularization
- Lasso
- Ridge
- ElasticNet
- Cross Validation
- Methods
- Sigmoid Function
- Entropy
- Information Gain
- Gini Index
- Feature Selection
- Ensemble Modeling (Bagging | Boosting | Stacking)
- K Selection / Distance Metrics
- Kernel Functions
- Elbow Method
- Metrics
- MAE / MSE/ RMSE / R2 and Adjusted R2
- AUC ROC CURVE
- Precision
- Recall
- F1 score
- Confusion Metrics
- Only Theory
- SMOTE and near miss
- EXTRA TREE CLASSIFIER (Random forest)
- TPOT
- LOG LOSS
- CROSS VALIDATION
- KERNAL PCA
- LIGHT GBM and cat boosting
- DBSCAN
- MINI BATCH K MEANS
- Passive Aggressive Classifier Algorithm
- Facebook Prophet
- time series RNN LSTM
- When to Use Which Algorithms!
- Projects / Case Studies
- 1) Real Estate Price Prediction
- 2) Fraud Detection Problem in BFSI domain
- 3) Disease Detection in Healthcare domain
- 4) Market Segmentation in Advertising Sector
- 5) Market Basket Analysis in Departmental Sector
- 6) Passenger Forecasting in Aviation Sector
- 7) News Classification in Media Sector
- 8) Dashboard (Visualization Tool)
- Please Note - Case studies / projects changes from time to time and will be covered along with their respective modules.
- Intro to AI & Neural Networks
- Deep Learning vs Machine Learning
- Tech Advancement
- All about Artificial Neural Networks (ANN)
- Understand How Deep Neural Network Works?
- Different variants of Gradient Descent
- Stochastic Gradient Descent vs Adam vs Others
- Hyper parameter Tuning
- Batch Size
- Learning Rate
- Momentum
- Deep Learning in Python
- Deep Learning packages in python
- Google TensorFlow Framework
- Model Building with default TFLearn API
- Keras Vs TFLearn Vs Pycharm APIs
- Model Building with Keras API Wrapper
- Activations
- Optimizers
- Losses
- Validation
- Evaluation Metrics
- Keras Backend
- Callbacks - Early Stopping, TensorBoard
- CNN - Convolutional Neural Networks
- Understanding Architecture & Visualizing a CNN
- Stochastic Gradient Descent vs Adam vs Others
- Hyper parameter Tuning
- Batch Size
- Learning Rate
- Momentum
- Transfer Learning
- vgg16
- Activations
- Optimizers
- Losses
- Validation
- Evaluation Metrics
- Keras Backend
- Callbacks - Early Stopping, TensorBoard
- vgg19
- Kernel
- Depth
- Pooling
- resnrt 50
- NLTK
- Simple NLTK
- Stemming
- Lemitaization
- Regex
- Stopwords
- Corpus
- Unigram
- Bigram
- Trigram
- BAG fo words(count vectorization)
- TD-IDF-term frequency inverse document frequency 1
- Word embedding
- word 2 vec --cbow / skipgram
- glove
- Fasttext
- Keyed vector
- LSTM / RNN (bidirentional)
- GRU(theory - Arthiecture build )
- Projects & Case Studies
- Image Recognition using MNIST Dataset
- Object Recognition using CIFAR-10 Dataset
- Speech Recognition (Google Voice | Alexa | Siri)
- Sentiment Analysis and Word Clouds
- Chat Bot Building
- Please Note - Case studies / projects changes from time to time and will be covered along with their respective modules.
- Oracle 18/21c Installation
- Basics
- Oracle Basics
- Database models
- ER Model Overview
- Data types
- Understanding Test Database
- Basics Queries
- Arithmetic and String functions
- DML Operations - Insert, Update and Delete
- FETCH command - Top N, Top % Rows
- Writing Queries
- Filters
- DISTINCT
- BETWEEN
- IN
- LIKE
- NOT
- NULL
- AND, OR, NOT etc
- Using Complex Data Filtering Techniques
- Sorting
- Ascending Order
- Descending Order
- Complex Sorting
- Functions
- String Functions - lower case, uppercase, substring, instring, trim etc.
- Number Functions
- Date Manipulation
- Null Value Functions - NVL, NVL2, NULLIF, COALESCE
- Conditional Expressions - Case and Decode
- Groups
- Basics
- Grouping functions - AVG, MIN, MAX, COUNT, DISTINCT COUNT etc
- Grouping Filters - HAVING
- Rollups & Cubes
- Analytic (Window) Functions
- Over() with summary functions
- Partition By Queries
- ROW_NUMBER()
- RANK()
- DENSE_RANK()
- Joins
- Cartesian Product
- Equi and Non-Equi Joins
- Left Outer Join
- Right Outer Join
- Full Outer Join
- Self Join
- Complex Queries - Joins & Groups Integration
- SET Operators
- Union
- Union All
- Intersect
- Minus
- Subquery
- Single Valued Row Sub Query
- Multi Valued Sub Query
- Views
- Inline Views
- ROWID & ROWNUM
- TOP N Analysis or Complex Inline View
- DDL
- Tables - Create, Alter, Drop
- Indexes - Types, Create, Drop
- Constraints - Not Null, Primary Key, Unique Key, Foreign Key
- Sequence
- Synonyms
- Foreign Keys
- DML operations - Insert, Update & Delete
- Insert, Update, Delete & Truncate
- Common Operations
- Creating Tables using queries
- Bulk Data Inserts using Queries
- Transactions ( T SQL)
- Commit
- Rollback
- Savepoint
- DCL - Grant & Revoke
- Project
- Installation and Configuration
- Introduction to Tableau
- Architecture
- Data Model
- Data Connection with single data sources
- Data Joins
- Live vs extract connection
- Working with multiple data sources
- Data Blending
- Difference between joining and blending data
- Tableau Interface
- Measures and dimension
- Shelves
- Show me
- Data Types
- Default Properties
- Marks Card
- Page Shelf
- Saving and sharing your work-overview
- Difference between .twb and .twbx
- Discrete vs Continuous
- Date Aggregations and Date parts
- Cross tab
- Totals & Subtotals
- Chart Types
- Bar Charts & Stacked Bars
- Pie Chart
- Line Graph with Date
- Line Graph without Date
- Area charts
- Tree Map
- Word Cloud
- Packed Bubble
- Box & Whiskers Plot
- Scatter Plots
- Highlight tables
- Heat maps
- Data Visualization
- Geographical fields
- Map options
- Run Time Columns
- Calculated Fields
- String Calculation
- Data Calculation
- Logical Calculation
- Numeric Calculation
- Parameters
- Table Calculations
- Level of detail
- Data Formatting
- Legends
- Highlights
- Labelling
- Number Formats
- Alignment
- Advanced Functionalities
- Sorting
- Groups
- Sets
- Hierarchy
- Reference and Trend Lines
- Edit Axis
- Bins
- Filters
- Dimension Filter
- Measure Filter
- Filter - Display Option
- Context Filters
- Relevant Filters
- Sets in filters
- Conditional Filters
- Advanced Charts
- Pareto
- Waterfall
- Funnel
- Waffel Chart
- Dashboards
- Dashboard Objects
- Dashboard Actions
- URL
- Highlight
- Filter
- Advanced Interactivity
- Publish to Web
- Report designing & exporting
- Case Studies / Project
- Data Visualization & Analytics
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- Data Sources
- from SQL (MySQL)
- from Flat Files (CSV / Excel etc.)
- from Python
Tools You Will Learn
Why get Trained from us ?
Training From Experts
Personal Attention
80% Practical
Study Material
Course Completion Certificate
Live Projects /
Case Studies
Interview Preparation
Best Placement Service
Data Science Projects
Real Time Projects on Data Analysis covered in our Data Science Training.
