Best Data Science Course in Amravati (Hybrid Classroom Training)

Data Science and Analytics is one of the most in-demand job roles for all companies today. There are over 30,000+ job openings for various roles in the field of Data Science. Get hands-on practice in Python, Tableau, Structured Query Language (SQL), Excel and various other Data Science concepts and tools from scratch.

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Highlights of our Data Science with Python Program

Industry accepted certification program Specifically Designed For College Student

LMS Access to get access over recorded lecture and resources

1:1 real time industry expert developer access for doubt solving and project building

360+ hrs of Project based Learning where 70% will be practical and 30% theory

Advance MERN stack technology covered along with Git and DSA

5+ capstone projects 2 are Individual project and 3 are group projects

6+ yrs of expert trainer to master you in the fundamental and concepts.

Practical assignment and group hackethones

Upcoming Batch Details

Duration Timings
(Monday - Saturday) 5 MONTH 08:51 AM to 09:51 AM

Book your free class now and learn with the best development institute in Amravati

Curriculum of Data Science with Python Course

  • Applications of Data Science
  • Python Introduction
  • Operators and Variables in Python
  • Data Types in Python
  • Control Flow in Python I
  • Control Flow in Python II
  • Functions in Python I
  • Functions in Python II
  • Packages and Modules in Python
  • File Handling in Python
  • Introduction to NumPy Arrays
  • Basic NumPy Operations
  • NumPy Functions
  • Indexing and Slicing of NumPy Arrays
  • Array Manipulation in Python I
  • Array Manipulation in Python I
  • File Handling using NumPy
  • NumPy Case Study
  • Introduction to Pandas Library in Python
  • Pandas Data Structures
  • Importing and Exporting Data Using Pandas
  • Functionality of Pandas Series
  • Functionality of Pandas DataFrames I
  • Functionality of Pandas DataFrames II
  • Combining Data using Pandas I
  • Combining Data using Pandas II
  • Data Cleaning using Pandas I
  • Data Cleaning using Pandas II
  • Grouping Data using Pandas I
  • Grouping Data using Pandas II
  • Data Visualization Library – Matplotlib
  • Data Visualization Library – Seaborn
  • Visualizing Matplotlib Plots and Charts
  • Customizing Visualizations and Saving Plots

  • Introduction to Tableau
  • Data Connections and Charts
  • Data Granularity and Sorting
  • Data Grouping and Filtering
  • Data Blending
  • Joins and Unions
  • Calculations in Tableau
  • Functions in Tableau
  • Table Calculations and Parameters
  • LOD Calculations
  • Trend Lines and Reference Lines
  • Forecasting and Clustering
  • Introduction to Mapping
  • Web Mapping Service (WMS)
  • Using Charts Effectively
  • Introduction to Dashboards in Tableau
  • Dashboard Layouts and Formatting
  • Interactive Dashboards
  • Story Points in Tableau
  • Visual Best Practices

  • Introduction to Deep Learning
  • Introduction to Neural Networks
  • Single Layer Perceptron
  • Multilayer Perceptron (MLP)
  • How Does a Neural Network Learn?
  • Backpropagation
  • Introduction to TensorFlow
  • LU2 - MNIST Digit Classification Using TensorFlow 2.x
  • Understanding CNN
  • Image Recognition
  • Introduction to RNN
  • Architecture of RNN
  • Understanding RNN
  • Drawback of Backpropagation
  • LSTM – Long Short-Term Memory Networks
  • Understanding LSTM Structure
  • Introduction to Reinforcement Learning (RL)
  • Understanding Reinforcement Learning
  • RL Agent Taxonomy
  • OpenAI Gym

  • Statistical Analysis in Data Science
  • Measures of Central Tendency
  • Measures of Dispersion
  • Measures of Position
  • Univariate Non-Graphical EDA
  • Univariate Graphical EDA
  • Multivariate Non-Graphical EDA
  • Multivariate Graphical EDA
  • Introduction to Probability Theory
  • Probability Events
  • Types of Probabilities
  • Bayes' Theorem
  • Probability Distributions
  • Skewness and Kurtosis
  • Types of Probability Distributions
  • Sampling Distributions
  • Inferential Statistics
  • Confidence Interval
  • Statistical Hypothesis Testing
  • P-Value and Critical Value
  • Hypothesis Tests
  • T–Tests
  • Chi–Squared Tests
  • Probability and Statistics Case Study

  • Model Selection
  • K-Fold Cross Validation
  • Model Evaluation
  • Model Evaluation Metrics for Regression
  • Model Evaluation Metrics for Classification
  • Calculating A Confusion Matrix
  • ROC and AUC
  • Precision, Recall and F1 Score
  • Hyperparameter Tuning
  • Hyperparameter Optimization
  • Perform Grid Search
  • Ensemble Learning
  • Bagging
  • Boosting
  • AdaBoost I
  • AdaBoost II
  • Gradient Boosting
  • XGBoost
  • Model Optimization
  • Linear Programming
  • Formulating Optimization Problem
  • Predicting Promotion Using Boosting Techniques

  • Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Other Techniques of Dimensionality Reduction
  • Unsupervised Learning Using Clustering
  • Hierarchical Clustering
  • K Means Clustering - I
  • K Means Clustering - II
  • Fuzzy C Means Clustering
  • DBSCAN Clustering
  • Association Rule Mining
  • Generating Association Rules
  • Apriori Algorithm
  • Market Basket Analysis
  • Recommendation Engine
  • Types of Recommender System
  • Recommending Similar Movie to the User
  • Introduction to Time Series
  • Types of Data
  • Checks for Stationarity Of Data
  • Convert Non-Stationary Data to Stationary Data
  • Time Series Models
  • Time Series Models Using Python
  • Case Study - Association Rule Mining and Time Series

  • Introduction to Machine Learning
  • Types of Machine Learning
  • Data Pre-processing Techniques I
  • Data Pre-processing Techniques II
  • Testing and Training Data
  • Supervised Learning: Regression
  • Linear Regression
  • Calculation of R Square
  • Gradient Descent
  • Regularization Techniques
  • Regression Case Study
  • Classification Algorithms
  • Logistic Regression
  • Decision Tree
  • Decision Trees with CART Algorithm - I
  • Decision Trees with CART Algorithm - II
  • Random Forest
  • Performance Measurements
  • Naïve Bayes Classification
  • How Naïve Bayes Works?
  • K Nearest Neighbor
  • K in KNN Algorithm
  • Support Vector Machine
  • Non-Linear SVMs

  • Introduction to Natural Language Processing (NLP)
  • Natural Language Tool-Kit (NLTK)
  • Text Pre-processing - I
  • Text Pre-processing - II
  • Text Pre-processing - III
  • Text Pre-processing - IV
  • Feature Extraction I
  • Feature Extraction II
  • Sentiment Analysis
  • Case Study - Sentiment Analysis
Benefit of learning from us
  • 300+ Hours of online content
  • Personalised mentorship sessions
  • Dedicated career support
  • 9+ languages & tools
  • 1:1 Doubt-solving with expert industry mentors
  • Proactive programme support
  • Industry accepted certificate
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Placement Plus

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25+ Mock Interview Preparation
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Professional Job Ready Resume & LinkedIn Profile Building
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5+ Job Opportunity Connect
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1:1 Career Guidance Session By Industry Expert
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20+ Hrs Of Aptitude, Logical Reasoning & Critical Thinking Session By Experts
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30+ Hrs Of Soft Skill And Professional Communication Skill Preparation
Placement Assistance

Internship Plus

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25+ Mock Interview Preparation
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Work On Live Projects And Gain Real Working Experience
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3+ Job Opportunity Connect
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1:1 Career Guidance Session By Industry Expert
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20+ Hrs Of Aptitude, Logical Reasoning & Critical Thinking Session By Experts
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30+ Hrs Of Soft Skill And Professional Communication Skill Preparation
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In Depth Learning With Hands On Project
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In Depth Learning With Hands On Project
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6+ Yr Experienced Industry Expert Trainer
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72+ Hrs Of Live Mentorship Session By Industry Expert

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Online Data Science with Python Course FAQs

Business analytics (BA) is a set of disciplines and technologies for solving business problems using data analysis, statistical models and other quantitative methods. It involves an iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis, to drive decision-making.

We provide detailed training on Business Analytics syllabus. Industry experts with more than 4 years of experience will be your mentor. We have practical sessions for providing you the real-world knowledge and make you job ready. We have doubt-solving sessions, mock interview preparations and placement assistance. You can always reach to your mentor of Business Analytics for any doubt you have regarding the syllabus, practicals or sessions.

Yes! Business Analytics is a great option for you, your department or industry doesn’t matter. Whether you are a fresher or a working professional who is now wishes to upgrade your level, Business Analytics can definitely take you to greater heights.

To get admission in the course get in touch with the counsellor or reach the nearby Techmomentum offline office. Contact at +91 8421432551 . Or email at: - enquiry@techmomentum.in

Upon completing a Business Analytics course from TechMomentum Offline, you'll have a range of career options to consider. Some potential paths you can explore are Data Analyst, Business Analyst, Data Scientist, Financial Analyst, Market Research Analyst, Healthcare Analyst, Supply Chain Analyst, Markting Analyst, Operations Analyst and more.

Business Analytics is a broad domain encompassing numerous new and evolving concepts. To achieve mastery in these multifaceted areas and become proficient with the associated tools, we strongly advise our learners to dedicate a minimum of three hours daily to their studies and practice.

a. There are more than 253K+ jobs in Business Analytics and there are chances that there will be rapid growth in the job opportunities.

b. The trends of Business Analytics are Data Analytics, Agile methodologies, Business Transformation, Cross-functional Collaboration, and Cybersecurity.

c. The Business Analytics market size was valued at USD 81.46 billion in the previous year and is expected to reach USD 130.95 billion in the next five years, registering a CAGR of 8.07% during the forecast period.

a. To address your queries, our trainers are readily available to you upon request.

b. Additionally, you can participate in our weekly doubt-solving sessions.

c. You can also post your questions in the doubt-solving groups for assistance.

Yes, Techmomentum provides Placement and Internship Plus programs, thoughtfully designed to offer personalized guidance and training from our dedicated Mentors to help you secure a position in your desired company. These programs are structured to prepare you for interviews by enhancing your profile, refining your resume, conducting employability assessments, facilitating group discussions, conducting mock interviews, and much more. What's even better? Upon enrolling in these programs, you receive the assurance of three internship opportunities or 100% job assistance.

a. Our placement support program, featuring resume building and mock interview preparation, is designed to enhance your job placement opportunities.

b.Internship access: - we have tie ups with multiple startup and companies with whom you will get a chance to get exposure of real time project which will increase your chances of getting a job.

c. Increase your chances of success in job interviews and career progression with our comprehensive spoken English communication sessions, professional communication program, and quality aptitude preparation. These resources provide a competitive advantage over other candidates.

No need to worry, you'll have access to recorded sessions for all live classes, ensuring you can catch up if you miss a session or wish to review the content. Additionally, in upcoming classes, you'll have the opportunity to address any questions or concerns directly with the instructor.

Yes. We are providing free demo classes with our Business Analytics certification course online.

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It’s time for you to learn Python programming and grab the opportunities!

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