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About Our Data Science & Adv. A.I. Certification Course -
Learn from basics of Python to advanced A.I. and Analytics modules with projects to become a Data Science Professional in short duration.
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Job Opportunities -
Data Science Engineer, Machine Learning Engineer, Data Analyst etc.
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Pre-requisites -
None. Any one from IT & non IT can learn.
- Related Course - Adv. Data Science & Analytics JP Program
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Syllabus
Data Science & Anaytics Course
- Oracle 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
- 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
- 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
- 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 using Inline Views
- Transactions ( T SQL)
- Commit
- Rollback
- Savepoint
- DCL - Grant & Revoke
- Project
- Introduction & Installation
- Introdution
- Installing Python
- Installing Pycharm
- Configurations
- Programming Basics
- Variables
- Data Types
- User Inputs
- Unpacking
- Boolean Practice
- What If, Else and If, Making Decisions
- For Loops
- Break Continue Pass
- Break Vs Continue Vs Pass
- Data Structures
- List
- Tuple
- Set
- Logic Building & Programming
- Pattern Programs
- Prime Number
- Fibonacci Sequence
- Factorial
- Recursion
- Swap 2 Variables
- Functions
- Creating Functions
- Functions Arguments
- Type of Arguments
- Keyworded Variable Length Arguments
- Using Built in Functions
- Global Keywords
- Passing List to Functions
- Anonymous Functions / Lambda
- Filter / Map Reduce
- Decorators
- Modules
- OOPs Programming
- Modules
- Special variable _name_
- Class & Object
- _init_ method
- Constructor, Self and Comparing Objects
- Inheritance
- Polymorphism:
- Method Overloading
- Method Overriding
- Exception Handling
- File handling
- MySQL
- Installation
- CRUD Operations (create, read, insert, update, delete)
- Database Integration with Python
- Connection to Database from Python
- Writing functions to handle database operations
- CRUD Operations in python
- Web Scrapping
- Beautiful SOUP library
- Parsing HTML files
- Getting Data from Any Website
- Python for Data Science -
- NumPy & Pandas
- Installing & Understanding Analytics Packages
- NumPy Array Operations
- Pandas Data Frame Operations
- Data Acquisition (Import & Export)
- Indexing
- Selection
- Sorting
- Filtering
- Group By
- Binning
- Concatenation
- Merge
- Data Visualization
- Installing Matplotlib
- Charts & Plots
- Histogram
- Scatter Plots
- Box Plots
- Line Chart
- Bar Chart
- Pie Chart etc.
- Mean, Median, Mode, Variance, Standard Deviation
- Skewness, Kurtosis, Correlation, Covariance
- Probability Distributions: Normal, Binomial, Poisson
- Sampling, Central Limit Theorem
- Hypothesis Testing: t-test, z-test, ANOVA, chi-square
- Confidence Intervals, p-values, Type I & II errors
- Note – Statistics part will be taken along with ML & AI as and when required
- Data Preprocessing & EDA Overview
- Handling missing values, outliers, duplicates
- Encoding (One-hot, Label), Scaling (Standard, MinMax)
- Feature engineering, selection techniques
- EDA: Univariate, Bivariate, Multivariate analysis
- Visuals: Heatmaps, Pairplots, Boxplots, Violin plots
- Supervised Learning
- Linear & Logistic Regression
- Decision Tree, Random Forest, KNN
- SVM with kernel functions
- Naive Bayes, AdaBoost, XGBoost
- Project 1: House Price Prediction
- Project 2: Email Spam Detection
- Unsupervised Learning
- K-Means, DBSCAN, Hierarchical Clustering
- Dimensionality Reduction: PCA
- Project 3: Employee Role Segmentation
- Model Evaluation & Optimization
- Metrics: Accuracy, Precision, Recall, F1, AUC, R2, RMSE
- Cross Validation, Stratified K-Fold
- Hyperparameter tuning: GridSearchCV, RandomizedSearchCV
- Imbalanced Data Handling: SMOTE, NearMiss
- Time Series Analysis Basics
- Trend, Seasonality, Noise, Stationarity
- ADF Test, ARIMA, SARIMA
- Facebook Prophet, Exponential Smoothing
- Project 4: Sales Forecasting
- NLP Basics
- Text preprocessing: Tokenization, Lemmatization, Stopwords
- BoW, TF-IDF, Word2Vec
- Text Classification, Sentiment Analysis
- Project 5: Sentiment Analysis of Product Reviews
- Artificial Neural Networks (ANN)
- Perceptron, Multilayer Perceptron
- Forward & Backward Propagation
- Activation, Loss functions, Optimizers
- Overfitting, Regularization, Batch Normalization
- Project 6: Iris Flower Classification using ANN
- Computer Vision (CNNs for Image Processing)
- Convolution, Pooling, Padding
- Transfer Learning (VGG, ResNet)
- R-CNN, Faster R-CNN
- Project 7: Handwritten Digit Recognition
- RNNs & LSTMs
- RNN, LSTM, GRU, Bidirectional RNN
- Sequence Modeling, Time Series, Text Generation
- Project 8: Next Word Prediction using RNN & LSTM
- Attention Mechanism, Transformer Architecture
- Using Pre-trained Models (BERT, RoBERTa)
- Embedding generation & fine-tuning
- Project 9: Sentiment Analysis using BERT (Transformers)
- GenAI
- What is GenAI? Prompt Engineering
- Use cases: Q&A, Summarization, Image Generation
- Ethical concerns & safety
- LLM Apps with OpenAI & LangChain
- Using ollama
- LangChain Pipelines, FAISS, ChromaDB
- Chat with PDF, CSV, Excel
- Project 10: Flow Stack Machine Learning Project
- Web Deployment: Streamlit or Flask
- Create AWS Instance
- Model Deployment on AWS
- Note - this module will be mostly online as trainers aare rare.
- Introduction to Power BI
- What is Power BI and Why Power BI
- Installing Power BI Desktop
- Exploring the Power BI Workflow
- Adjusting the settings of the Power BI Desktop
- Comparison of Power BI vs Other Reporting Tools
- Getting and Transforming Data with Power BI Desktop
- Connecting to different sources
- Connecting to multiple sources
- Different connecting Options(DirectQuery vs Import Data Vs Live Connection)
- Shaping and transforming data with Power Query
- Editing,Merging,Appending queries etc
- Advance Importing techniques without coding
- Data cleaning & Advance data cleaning
- Modeling with Power BI
- Introduction to Modeling
- Building Relational Models(Setup and Manage Relationships)
- Creating table relationships
- Understanding the filter flow
- Cardinality and Cross filtering
- DAX
- Understanding Dax Syntax
- Calculated Columns vs Measures
- Common Dax functions and formulae
- Understanding the evaluation context in DAX
- Visualising Data with Reports
- Creating Visualisations
- Color & Conditional Formatting
- Setting Sort Order
- Scatter & Bubble Charts & Play Axis
- Tooltips
- Slicers, Timeline Slicers & Sync Slicers
- Cross Filtering and Highlighting
- Visual, Page and Report Level Filters
- Drill Down/Up
- Hierarchies
- Constant Lines
- Tables, Matrices & Table Conditional Formatting
- KPI's, Cards & Gauges
- Map Visualisations
- Custom Visuals
- Managing and Arranging
- Drillthrough
- Custom Report Themes
- Grouping and Binning
- Bookmarks & Buttons
- Introduction to Power BI Service
- Introduction to the Power BI Service
- Quick Tour of Power BI Service
- Connecting to Data from Power BI service
- Building Blocks of Power BI Service
- Sharing and Collaboration Tools in Power BI Service
- Sharing and Collaboration Options Overview
- Publish from Power BI Desktop
- Publish Reports to Web
- Printing and Exporting from Power BI Service
- Sharing Reports & Dashboards
- Power BI Refreshing Datasets
- What is refreshing?
- Implementation
- A.I. in Power BI
- Advance Report techniques
- Power Automate (Latest)
- Introduction
- installation
- Types of flows
- Actions
- Copy multiple excel, pdf files from one folder
- Merge PDF files together, Rename etc.
- Extract phones name and price from amazon
- Best Practices
- Pkg1 - 3.5 to 4 months - Py + DS & AI
- Pkg2 - 6 to 7 months - Full Course - DS & Analytics
- Pkg2 - 4 to 5 months- (Full Course, Early Completion) - SQL & Power BI can be taken in parallel one at a time either on same day or on weekends to complete course earlier. Please inquire with institute for more info.
- Note - Addon topics (Adv. AI) - duration and fees extra
- Tableau / Adv Excel can be added
- Individual Courses / Customized Training Available
- more info
Tools
Google Reviews
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Aishwariya Tari
I have enrolled for data science course. Excellent teaching and supportive environment. Overall a positive experience with the institute. Apurva mam taught us sql and python explaining every concept in clear and concise manner. Aditya sir teaches tableau in wonderful way. He ensures the doubts are cleared and easier to understand. Tejas Sir taught machine learning and AI in an excellent manner by giving apt examples to make us understand the concepts in an easier way. -
Pradeep Unde
The emphasis on practical training sets Quick Expert Infotech apart, providing a 100% practical training environment. The trainers are not only skilled but also incredibly cooperative, ensuring that every trainee gets the hands-on experience necessary for success in the field. Trainee Tejas sir's guidance has been instrumental in navigating the complexities of data science, making the learning journey both engaging and enriching. I appreciate the commitment of Quick Expert Infotech to delivering quality education, and I am confident that the skills and knowledge I am gaining here will be invaluable in my IT career. I highly recommend Quick Expert Infotech to anyone seeking a comprehensive and practical approach to data science training. -
Mayuri Gujar
I have join QuickXpert infotech for Data science.I got trainer Shreya mam . She is very patient which anyone that takes a language needs – and it’s very comfortable to practice, which is also a huge plus. I recommend her highly. She is an excellent teacher! She provides both a great mix of listening, speaking and practical learning activities. Her manner of teaching is so wonderful and refreshing!! Thank you..! -
Aishwariya Alure
In this Data science course, I have learnt many things and enjoyed as well. Lessons are well structured. Incorporating real-world examples in classes to help us understand the concepts. I am happy to have you as my mentor for this course. Thank you for this memorable classes. -
Prapti Tamboli
I Join QuickXpert Infotech for Data Science Course. And recently I have completed my course, My heartfelt Thank You to Shreya ma'am for guiding us through this whole course. Your teaching technique makes everything easier for us, to understand each concept properly. You are the best mentor anyone could ask for!! Thank You ma'am!! -
Sonali Ghadage
Excellent training institute for data science course. I have join quickexpert for data science,Mrs. Shreya Jain ma'am is our trainer to teach us Data Science. She teach us very friendly and easy way by which we can understand the concept and use them. If we have a doubt and in one time our doubt is not clear then mam will clear our doubt several times until our doubt gets cleared also gives other extra knowledge for our betterment. The quality of the subject that we got from her is outstanding..The quality of the subject that we got from her is outstanding & I am recommend everyone to learn data science & Analyst skills only shreya maam -
Rahul Salunkhe
I joined QuickXpert and I completed DataScience course here, it was very good experience to learn here, all the queries and doubts are cleared by Mrs. Shreya Jain at the same time and I have done Practice more than many projects, like Titanic, Flight price predictions , HR Attribute etc. -
Nikhil Chikhalkar
I completed a comprehensive Data Science course at Quick Expert Institute, and I am thoroughly impressed with my experience. The course content was well-structured, covering all the essential concepts and tools required for data science, such as Python, machine learning, statistics, and data visualization.What sets Quick Expert Institute apart is their dedicated placement support. -
Aniket Magar
I'm pursuing data science course from Quick expert infotech under trainee Tejas sir. This is one of the finest training centre for the IT sector. The faculty and management is always ready to support.100% practical training environment with skilled cooperative trainers. -
Pragati Namde
I Join QuickXpert Infotech for Data Science Course. And recently I have completed my course, My heartfelt Thank You to Tejas Tambe sir for guiding us through this whole course. Your teaching technique makes everything easier for us, to understand each concept properly. You are the best mentor anyone could ask for!! Thank You sir !I
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For faster outcomes, we initiate placements early during training (eligibility-based) or at 80–90% course completion, with end-to-end placement support and continuous expert guidance at every step to help you succeed. Because,
"Your Career, Our Priority"
Enroll → Learn → Succeed!
Successfully helping students get placed since 2014.
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