Speciality
Prof_Trained

15000+ Professionals Trained

Batch-every

3+ Batches every month

Prof_Trained

50+ Industry expert trainers

Corporate

2500+ Corporate Served

Key Reasons for Choosing Uncodemy

The significant advantages to support the case for enrolling as soon as feasible in Uncodemy's Mentorship on Certified Data Science course with Placement Assistance, by paying a little fee to a company that hires industry-leading tutors.Whether you're looking for an offline Data Science course or a top-notch Data Science institute with placement support, Uncodemy offers in-depth coaching and classes tailored to your needs. Our Data Scientist course with placement support is meant to provide you the abilities and information need to succeed in this industry. Enroll in our courses on data science to realize your full potential in the field. Don't pass up this chance to enroll in our Data Science courses and advance your career.

Joining Uncodemy is a great investment because we provide quantity of features and perks that make us one of the Best Data Science institutes. Several compelling factors suggest selecting Uncodemy:

Expert trainers with real-world experience.
Comprehensive curriculum covering all aspects of Data Science.
Hands-on projects to strengthen practical knowledge.
Flexible course timings for working professionals.
Offline Data Science courses with personalized attention.
Industry-relevant certifications.
Affordable course fees with easy payment options.
Access to state-of-the-art learning resources.
Practical training with advanced tools and technologies.
Job-oriented courses with placement assistance.
Regular workshops and webinars by industry experts.
Dedicated career support and interview preparation.
Small batch sizes for better learning.
Dynamic learning environment with peer support.
Strong alumni network and industry connections.

Curriculums for Data science Training Courses

Data Science Curriculum

The curriculum has been designed by faculty from IITs, and Expert Industry Professionals.

time
150+

Hours of Content

live1-removebg-preview
90+

Live Sessions

tools
15+

Tools and Software

Set the Basics Right

Uncodemy is well known for offering elite Data Science training. Our extensive program has been expertly created to guarantee that you gain the theoretical understanding and practical expertise required to succeed as a data scientist. Below is a comprehensive list of all the chapters, volumes, subjects, and units that are covered in our course of data science.

1. Python for Data Science

  • Need for Programming
  • Advantages of Programming
  • Overview of Python
  • Organizations using Python
  • Python Applications in Various Domains
  • Python Installation
  • Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Command Line Arguments
  • Method of Accepting User Input and eval Function
  • Python - Files Input/Output Functions
  • Lists and Related Operations
  • Tuples and Related Operations
  • Strings and Related Operations
  • Sets and Related Operations
  • Dictionaries and Related Operations
  • User-Defined Functions
  • Concept of Return Statement
  • Concept of name =” main ”
  • Function Parameters
  • Different Types of Arguments
  • Global Variables
  • Global Keyword
  • Variable Scope and Returning Values
  • Lambda Functions
  • Various Built-In Functions
  • Introduction to Object-Oriented Concepts
  • Built-In Class Attributes
  • Public, Protected and Private Attributes, and Methods
  • Class Variable and Instance Variable
  • Constructor and Destructor
  • Decorator in Python
  • Core Object-Oriented Principles
  • Inheritance and Its Types
  • Method Resolution Order
  • Overloading
  • Overriding
  • Getter and Setter Methods
  • Inheritance-In-Class Case Study
  • Standard Libraries
  • Packages and Import Statements
  • Topics : Working with Modules and Handling Exceptions
  • Info@uncodemy.com | +91-7701928515 | www.uncodemy.com
  • Reload Function
  • Important Modules in Python
  • Sys Module
  • Os Module
  • Math Module
  • Date-Time Module
  • Random Module
  • JSON Module
  • Regular Expression
  • Exception Handling
  • Basics of Data Analysis
  • NumPy - Arrays
  • Operations on Arrays
  • Indexing Slicing and Iterating
  • NumPy ArrayAttributes
  • Matrix Product
  • NumPy Functions
  • Functions
  • Array Manipulation
  • File Handling Using NumPy
  • Array Creation and Logic Functions
  • File Handling Using Numpy
  • Introduction to pandas
  • Data structures in pandas
  • Series
  • Data Frames
  • Importing and Exporting Files in Python
  • Basic Functionalities of a Data Object
  • Merging of Data Objects
  • Concatenation of Data Objects
  • Types of Joins on Data Objects
  • Data Cleaning using pandas
  • Exploring Datasets
  • 2. Data Science Primer and Statistics

  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Application of Data Science
  • Introduction
  • Stages of Analytics
  • CRISP DM Data Life Cycle
  • Data Types
  • Introduction to EDA
  • First Business Moment Decision
  • Second Business Moment Decision
  • Third Business Moment Decision
  • Fourth Business Moment Decision
  • Correlation
  • What is Feature
  • Feature Engineering
  • Feature Engineering Process
  • Benefit
  • Feature Engineering Techniques
  • Basics Of Probability
  • Discrete Probability Distributions
  • Continuous Probability Distributions
  • Central Limit Theorem
  • Concepts Of Hypothesis Testing - I: Null And Alternate Hypothesis, Making
  • A Decision, And Critical Value Method
  • Concepts Of Hypothesis Testing - II: P-Value Method And Types Of Errors
  • Industry Demonstration Of Hypothesis Testing: Two-Sample Mean And
  • Proportion Test, A/B Testing
  • 3. Machine Learning

  • Simple Linear Regression
  • Simple Linear Regression In Python
  • Multiple Linear Regression
  • Multiple Linear Regression In Python
  • Industry Relevance Of Linear Regression
  • Univariate Logistic Regression
  • Multivariate Logistic Regression: Model
  • Building And Evaluation
  • Logistic Regression:
  • Industry Applications
  • Data mining classifier technique
  • Application of KNN classifier
  • Lazy learner classifier
  • Altering hyperparameter(k) for better accuracy
  • Black box
  • SVM hyperplane
  • Max margin hyperplane
  • Kernel tricks for non linear spaces
  • Rule based classification method
  • Different nodes for develop decision trees
  • Discretization
  • Entropy
  • Greedy approach
  • Information gain
  • Challenges with standalone model
  • Reliability and performance of a standalone model
  • Homogeneous & Heterogeneous Ensemble Technique
  • Bagging & Boosting
  • Random forest
  • Stacking
  • Voting & Averaging technique
  • Difference between cross sectional and time series data
  • Different component of time series data
  • Visualization techniques for time series data
  • Model based approach
  • Data driven based approach
  • Difference between Supervised and Unsupervised Learning
  • Prelims of clustering
  • Measuring distance between record and groups
  • Linkage functions
  • Dendrogram
  • Dimension reduction
  • Application of PCA
  • PCA & its working
  • SVD & its working
  • Point of Sale
  • Application of Association rules
  • Measure of association rules
  • Drawback of measure of association rules
  • Condition probability
  • Lift ratio
  • 4. Deep Learning

  • Black box techniques
  • Intution of neural networks
  • Perceptron algorithm
  • Calculation of new weights
  • Non linear boundaries in MLP
  • Integration function
  • Activation function
  • Error surface
  • Gradient descent algo
  • Imagenet classification challenges
  • Convolution network applications
  • Challenges in classifying the images using MLP
  • Parameter explosion
  • Pooling layers
  • Fully connected layers
  • Alexnet case study
  • Modelling sequence data
  • Vanishing/Gradient descent explode
  • What is a Deep Learning Platform?
  • H2O.ai
  • Dato GraphLab
  • What is a Deep Learning Library?
  • Theano
  • Deeplearning4j
  • Torch
  • Caffe
  • 5. Data Visualization and Story Telling

  • Bar Charts
  • Histograms
  • Pie Charts
  • Box Plots
  • Scatter Plots
  • Line Plots and Regression
  • Pair plot
  • Word Clouds
  • Radar Charts
  • Waffle Charts
  • 6. Natural Language Processing

  • Text data generating sources
  • How to give structure to text structure using bag of words
  • Terminology used in text data analysis
  • DTM & TDM
  • TFIDF & its usage
  • Word cloud and its interpretation
  • 7. SQL

  • Introduction to Databases
  • How to create a Database instance on Cloud?
  • Provision a Cloud hosted Database instance.
  • What is SQL?
  • Thinking About Your Data
  • Relational vs. Transactional Models ER Diagram
  • CREATE Table Statement and DROP tables
  • UPDATE and DELETE Statements
  • Retrieving Data with a SELECT Statement
  • Creating Temporary Tables
  • Adding Comments to SQL
  • Basics of Filtering with SQL
  • Advanced Filtering: IN, OR, and NOT
  • Using Wildcards in SQL
  • Sorting with ORDER BY
  • Math Operations
  • Aggregate Functions
  • Grouping Data with SQL
  • Using Subqueries
  • Subquery Best Practices and Considerations
  • Joining Tables
  • Cartesian (Cross) Joins
  • Inner Joins
  • Aliases and Self Joins
  • Advanced Joins: Left, Right, and Full Outer Joins
  • Unions
  • Working with Text Strings
  • Working with Date and Time Strings
  • Date and Time Strings Examples
  • Case Statements
  • Views
  • Data Governance and Profiling
  • Using SQL for Data Science
  • How to access databases using Python?
  • Writing code using DB-API
  • Connecting to a database using DB API
  • Create Database Credentials
  • Connecting to a database instance
  • Creating tables, loading, inserting, data and querying data
  • Analysing data with Python
  • 8. Excel

  • Input data & handling large spreadsheets
  • Tricks to get your work done faster
  • Automating data analysis (Excel VLOOKUP, IF Function, ROUND and more)
  • Transforming messy data into shape
  • Cleaning, Processing and Organizing large data
  • Spreadsheet design principles
  • Drop-down lists in Excel and adding data validation to the cells.
  • Creating Charts & Interactive reports with Excel Pivot Tables, PivotCharts, Slicers and Timelines
  • Functions like: - COUNTIFS, COUNT, SUMIFS, AVERAGE and many more.
  • Excel features: - Sort, Filter, Search & Replace Go to Special etc...
  • Importing and Transforming data (with Power Query)
  • Customize the Microsoft Excel interface
  • Formatting correctly for professional reports.
  • Commenting on cells.
  • Automate data entry with Autofill and Flash-fill.
  • Writing Excel formulas & referencing to other workbooks / worksheets.
  • Printing options
  • Charts beyond column and bar charts: - Pareto chart, Histogram, Treemap, Sunburst
  • charts & more
  • 9. Tableau

  • Introduction to Data Visualization
  • Tableau Introduction and Tableau Architecture
  • Exploring Data using Tableau
  • Working with Data using Tableau including Data Extraction and
  • Blending
  • Various Charts in Tableau(Basics to Advanced)
  • Sorting-Quick Sort, Sort from Axis, Legends, Axis, Sort by Fields
  • Filtering- Dimension Filters, Measure Filters, Date Filters, Tableau
  • Context Filters
  • Groups , Sets and Combined Sets
  • Reference Lines, Bands and Distribution
  • Parameters, Dynamic Parameters and Actions
  • Forecasting-Exponential Smoothening Techniques
  • Clustering
  • Calculated Fields in Tableau, Quick Tables
  • Tableau Mapping Features
  • Tableau Dashboards, Dashboards Action and Stories
  • 10. Power BI

  • Introduction to Power BI – Need, Imprtance
  • Power BI – Advantages and Scalable Options
  • Power BI Data Source Library and DW Files
  • Business Analyst Tools, MS Cloud Tools
  • Power BI Installation
  • Power BI Desktop – Instalation, Usage
  • Sample Reports and Visualization Controls
  • Understanding Desktop & Mobile Editions
  • Report Rendering Options and End User Access
  • Report Design with Databse Tables
  • Report Visuals, Fields and UI Options
  • Reports with Multiple Pages and Advantages
  • Pages with Multiple Visualizations. Data Access
  • “GET DATA” Options and Report Fields, Filters
  • Report View Options: Full, Fit Page, Width Scale
  • Report Design using Databases & Queries
  • The Data Science Course from Uncodemy is a great option for anyone looking for practical experience with in-person instructor support because it provides an offline training approach. Our course material is tailored to meet the needs of both beginning and experienced students, guaranteeing that you will get a thorough understanding of Data Science.

    In order to support you in making the smooth transition from education to career success, our course also includes placement aid. Uncodemy is the greatest center for Data Science training since it emphasizes both academic knowledge and practical skills, offering a comprehensive and perceptive education in this quickly developing sector.

    Obtain the World-Class Data Science Certification

    Obtaining a well-known certification might significantly impact your career trajectory in data science. A top-tier Data Science Certification from Uncodemy can help you stand out in this competitive field.

    With the help of our Data Science Certification, you will gain the in-depth knowledge and useful skills required to succeed in the field.

    Here are three key benefits of choosing Uncodemy for your Data Science training:
    • Comprehensive Curriculum: Our course covers all essential aspects of data science, from basic concepts to advanced techniques.
    • Expert Trainers: Learn from industry experts who bring a wealth of experience and practical insights to the classroom.
    • Placement Assistance: We provide dedicated support to help you secure a position after completing your certification..

    Association with-

    • ISO
    • NASSCOM
    • Skill India
    How Uncodemy Helps Every Student Acing Their Interview-

    At Uncodemy, we’re committed to ensuring that our students not only excel in their courses but also succeed in their job interviews. Here’s how we help every student prepare to impress potential employers and secure their dream job:

    Join Uncodemy

    Be a part of a vibrant community of learners and professionals who are passionate about Data Science. Network with peers, share knowledge, and grow together.

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    Deep Understanding of the Data Science Coursewith Live Projects

    With Uncodemy's Live projects, get a thorough understanding of the data science field. Our practical approach provides you with real-world experience and expertise, preparing you for the workforce. Find out how our course on data science will help you succeed.

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    Frequently Asked Questions

    For top-notch Data Science training, Uncodemy stands out. Their course offers comprehensive learning with practical insights, competitive fees, and excellent placement support, making it the best choice for aspiring data scientists in the region.

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    Uncodemy’s Data Science course covers essential topics like data analysis, machine learning, and statistical modeling. The syllabus is designed to equip you with practical skills and knowledge, ensuring a strong foundation for a career in data science.

    For the best data science course, Uncodemy stands out. Their program offers comprehensive training, expert instructors, and excellent placement assistance, making it an ideal choice for aspiring data scientists.

    No prior coding experience is needed to enroll in Uncodemy’s Data Science Course. Our training is designed to accommodate beginners and guide you through the basics to advanced concepts, ensuring a smooth learning journey.

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