Best Data Science Course Training in Mumbai - Affordable Fees - Online Classes Available
Learners 1000+
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Best Data Science Course in Mumbai to Learn in Online / Classroom Format from Best Data Science Training Institute QuickXpert Infotech known for Quality Training in Reasonable Fees and Placements!
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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 -
This course is targeted to both freshers and experienced who wish to become a Data Scientist! Course covers 10+ case studies across multiple industries to help you understand how to implement data science models for real time scenarios. In Analytics you will be learning both SQL and Analytics tool because data science output is represented Analytically using Analytical Tools like Tableau or Power BI. Hence the combination of Data Science and Analytics is most preferred in Industry with Excellent Pay Package!
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What You Will Learn ?
Python, Python Analytics, SQL, Tableau, Statistics, Data Analysis, M.L. Algorithms, Neural Networks, A.I., Tensor Flow, Keras, Advanced Libraries, Image Processing, Text Analytics, NLP etc.
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Duration & Fees -
1. Data Science & A.I. - 4 months - Rs.32000 (O) / Rs.35000 (C) (installments available)
2. Data Scientist or Data Science & Analyst - 6 months - Fees on request
Note - Customized Training available -
Best Placement Service -
Placement process starts on completion of 80% of course for Quick Turnaround.
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Upcoming Batch -
Please submit inquriy form
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Scope & Job Openings -
Highly Demanded
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Jobs Roles Targeted -
Data Scientist, Machine Learning Engineer, Data Engineer, Data Science & Analytics Engineer etc.
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Any Pre-requisites -
None. Any one from IT or non IT can learn. Students from any IT / Stats / Any Engg. Background Preferred.
Syllabus
Job Opportunity
Data Science & Analalytics Course
- Installation & Configuration
- Using Professional Tool - MySQL Workbench
- Create Database
- Create Database Objects
- Create Tables
- Implementing Foreign Keys
- Using Auto Increment
- Indexes & Index Types
- Constraints
- Foreign Keys & index
- Primary key & index
- Unique key & index
- Not null constraint
- DML Operations
- Insert
- Update
- Delete
- Select Queries
- Basic Queries
- Using AND & OR conditions
- Using in, between, distinct and like keywords
- Sorting in ascending and descending order
- Functions
- Trim
- Uppercase
- Lowercase
- LengthPosition
- Left
- Ifnul
- Substring
- Substringindex
- Date
- Numbers
- Convert
- Cast etc.
- Groups
- Understanding Grouping Columns Usage - Dos & Don’ts
- Grouping Functions o Min o Max o Sum o Count o Average o Distinct Count
- Using Having Clause
- Filtering on grouing functions
- Multi column groups
- Joins
- Creating Foreign Keys and use them in Joins
- Inner Join or Natural Join
- Left Outer Join
- Right Outer Join
- Full Outer Joins
- Complex Join Queries etc
- Views & Inline Views
- Subquery
- Create Complex Queries
- Using Joins, Groups, Views & Subquery all together
- Creating tables using queries
- Inserting, Update & Delete using Select Queries
- Mini Project / Case Study
- Installation and Configuration
- Introduction to Tableau
- Architecture
- Data Model
- Data Connection with single data sources
- Data Joins
- 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
- Case Studies / Project
- Data Visualization & Analytics
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- Data Sources
- from SQL (MySQL)
- from Flat Files (CSV / Excel etc.)
- from Python
- 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
- Stepwise Regression
- Logistic Regression
- Sigmoid Function
- Entropy
- Information Gain
- Gini Index
- Decision Tree (CART)
- Ensemble Learning
- Random Forest
- xgBoost
- K Nearest Neighbors (KNN)
- K Selection
- Distance Metrics
- Support Vector Machine (SVM)
- Kernel Functions
- Naive Bayes Classifier (NBC)
- Perceptron Learning
- Multi Layer Perceptron (MLP)
- Unsupervised Learning
- Hierachical Clustering
- Dendograms
- K Means Clustering
- Association Rule Mining
- Recommendation Engine
- Model Improvement & Validation
- Regularization
- Lasso
- Ridge
- ElasticNet
- Cross Validation
- ROC Curve
- Confusion Matrix
- Overfitting
- Underfitting Problems
- Precision vs Recall
- F1 Score
- Type I vs Type II Error
- Ensemble Modeling (Bagging | Boosting | Stacking)
- Feature Selection
- Feature Extraction
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- Multi class Classification
- Linear Discriminant Analysis (LDA)
- Scree Plot
- Elbow Method
- Time Series Forecasting
- Time Series Data
- Trend Chart
- Stationarity
- Seasonality
- Moving Average
- Exponential Smoothing
- ARIMA
- GARCH
- 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.
- End to End Data Analysis Project using Python, Tableau, SQL, M.L., Flat files etc.
- 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
- Kernel
- Depth
- Pooling
- RNN - Recurrent Neural Networks
- Recurrent Neural Network Model
- Training RNNs with Back-propagation
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Text Analytics / NLP - Natural Language Processing
- Understanding Text Mining & Analyti
- Tokenization
- Stop Word Removal
- Stemming
- Lexical Analysis
- 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.
Syllabus
Tools You Will Learn
Why get Trained from us ?
Training From Experts
Personal Attention
80% Practical
Training Material
Course Completion Certificate
Live Projects / Case Studies
Interview Preparation
Best Placement Service
Data Science / Data Analytics Placements
Frequently Asked Questions
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Quick info about us
9+ yrs in IT Training and Placements, Online and Classroom Trainings, ISO Certified, 1000s Trained! Please visit home page for more info. Fees if mentioned on page then valid only for today. Latest Fees apply. To book your seat, Enrol Today.
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How Online Lectures Will be Conducted?
Live Lectures On Gotomeeting, Googlemeet, Zoom etc. Trainers will be Live so your doubts will be solved live via screenshare.
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Classroom Training Location?
in Thane (Mumbai) opp. Thane west railway station.
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Training Certificate?
Yes. From our Institute on completion of course. We are an ISO 9001:2015 certified Institute and into Training Industry from 9+ years hence our certificate is acceptable across the industry.
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Placement Support?
Yes 100%! Many of Our Students Got Job WITHIN 2 months of Course Completion! Recent Placements - placement page.
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Upcoming Batch?
New batches starts approx every 15 to 20 days. Limited seats per batch. For more info submit inquiry form.
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What if i Miss a Session?
Either in Classroom or Online if you miss a session Recording of Lecture or repeat lecture as available will be provided.
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What if i have to take long breaks?
We do allow if conveyed to us in writing well in advanced and approved by us.
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Group Discounts?
Yes - Please talk to counsellor.
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Do Your Provide Corporate Trainings?
Yes. We have delivered corporate trainings to NMIMS, Infogix, GeBBS Healthcare solutions, PDG Softwares etc. Please submit inquiry form to convey your requirements.
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Any Other Queries?
Please submit inquriy form.
