1. Digiperform
Digiperform is a top-notch training provider in India, specializing in digital skills. Our comprehensive course structure is meticulously crafted by a team of over 50 industry experts in Data Science. We actively seek input and ideas from 450 forward-thinking businesses across Asia to keep our courses aligned with the latest industry trends.
Our curriculum is carefully tailored to cover essential skills for roles in both small and large Data Science firms, as well as for companies with their own Data Science teams. At Digiperform, we prioritize hands-on learning by blending practical exercises, research, and assignments to offer a thorough and pragmatic learning experience.
Why Choose Digiperform Online Data Science Course?
Opting for Digiperform’s Online Data Science Course in Dehradun ensures a distinctive and invaluable learning journey for budding data enthusiasts. Here’s why Digiperform stands as the ideal choice for your Data Science education in the serene city of Dehradun:
Industry Experts as Trainers: Mastering Data Science demands learning from the best. Our trainers are seasoned industry experts, boasting an average of 8+ years of hands-on experience in leading organizations. They bring forth real-world insights and expertise, enriching the virtual classroom with practical knowledge.
100% Placement Assistance: Education’s ultimate goal is securing a fulfilling career. Our dedicated Placement Cell ensures comprehensive support in landing your dream job in the Data Science realm. We’re committed to guiding you towards promising career opportunities.
60+ Hands-on Projects: Our emphasis on experiential learning is paramount. The course is designed with over 60 practical projects, fostering a 90% practical and 10% theoretical approach. This ensures you acquire the skills essential for real-world Data Science tasks.
Cutting-Edge Curriculum: Aligned with international standards, our curriculum offers the most advanced content. Access a treasure trove of resources—PPTs, documents, PDFs, exercises, quizzes, and projects—crafting a holistic learning experience.
Personalized Career Mentorship: Benefit from dedicated online support and weekly doubt-clearing sessions led by experts. This one-to-one mentorship caters to your unique career goals, providing tailored guidance.
24×7 Learning Management System (LMS): Enjoy round-the-clock access to lectures and training materials via our LMS. This flexibility allows learning at your pace and revisiting materials whenever necessary.
Simulation & Mock Interviews: We prioritize meeting industry hiring standards. Our course includes simulation and mock interviews, preparing you thoroughly for real Data Science job interviews.
Custom Interview Preparation: Data Science roles vary, and interview demands can differ widely. We offer specialized training, equipping you with the knowledge and skills necessary for diverse Data Science positions, ensuring readiness for any interview scenario.
Selecting Digiperform’s Online Data Science Course in Dehradun means choosing quality, flexibility, and a curriculum tailored to meet the specific needs of the city’s Data Science enthusiasts. Our commitment to empowering students with practical skills and industry-relevant knowledge positions us as the premier choice for your Data Science education journey in Dehradun.
Data Science Online Course: Advantages
Enrolling in Digiperform’s Data Science Online Course in Dehradun unlocks a myriad of exclusive advantages that distinguish our program. Here’s why opting for Digiperform for your Data Science education is a savvy and pertinent choice:
Industry-Tailored Curriculum: Our online Data Science course is intricately crafted to harmonize with the latest industry requisites and trends. You’ll delve into the most current tools, techniques, and technologies pertinent to the swiftly evolving data science landscape.
Practical Emphasis: At Digiperform, we prioritize hands-on learning. Engage in real-world scenarios through hands-on projects and assignments, ensuring you’re not just well-versed in theory but also job-ready.
Flexibility & Convenience: Recognizing the demands of modern life, our online courses offer the flexibility of learning at your pace, from any location, and at your preferred time. This adaptability empowers you to balance studies with other commitments seamlessly.
Tailored Support: Personalized learning is our ethos. Courses are meticulously designed to cater to individual needs and aspirations, guaranteeing a bespoke educational journey.
Seasoned Faculty: Learn from industry stalwarts who bring their practical insights and expertise to the virtual classroom. Benefit from their wealth of experience, gaining invaluable industry perspectives.
Dehradun Focus: Digiperform’s Data Science course accounts for the unique demands of the Dehradun market, equipping you with skills directly relevant to local job opportunities. This localized approach distinguishes our program.
Certification & Recognition: Completion of our course not only furnishes you with knowledge but also a certification acknowledged in the industry. This credential significantly amplifies your career prospects and potential advancements.
Career Guidance: Avail robust career assistance, including job placement support and comprehensive interview preparation. Our commitment extends beyond education to securing a fulfilling career in the data science domain.
Data Science Course Syllabus
Module 1: Introduction to Data Science |
Introduction to the Industry & Buzzwords Industrial application of data science Introduction to different Data Science Techniques Important Software & Tools Career paths & growth in data science |
Module 2: Introduction to Excel
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Introduction to Excel- Interface, Sorting & Filtering,
Excel Reporting- Basic & Conditional Formatting
Layouts, Printing and Securing Files |
Module 3: Introduction to Stats
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Introduction to Statistics & It’s Applications Intro: Inferential vs. descriptive statistics |
Module 4: Descriptive Stats Using Excel Datasets |
Categorical Variables Visualization Using Excel Charts- FDT, Pie Charts, Bar Charts & Pareto Numerical Variables Visualization of Frequency & Absolute Frequency- Using Histogram, Cross Table & Scatter Plot Measure of Spread ( Mean, Mode , Median) Measure of Variance( Skewness, SD, Variance, Range, Coef. Of Variance, Bivariate Analysis, Covariance & Correlation) |
Module 5: Inferential Stats Using Excel Datasets |
Introduction to Probability Permutation & Combinations Standard Normal distribution Normal vs. Standard Normal distribution Confidence Intervals & Z-Score Hypothesis Testing & It’s Types |
Module 6: Database Design & MySQL |
Relational Database theory & Introduction to SQL Database Creation in the MySQL Workbench Case Statements, Stored Routines and Cursors Ø Query Optimisation and Best Practices Ø Problem-Solving Using SQL |
Module 7: Data Visualization Using Advanced Excel
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Advanced Visualizations- PIVOT Charts, Sparklines, Waterfall Charts Data Analysis ToolPak – Regression in Excel |
Module 8: Data Visualization Using Tableau |
Tableau vs Excel and PowerBI Exploratory and Explanatory Analysis Getting started with Tableau Visualizing and Analyzing data with Tableau – I Visualizing and Analyzing Data with Tableau – II Numeric and String functions Logical and Date functions Histograms and parameters Top N Parameters and Calculated Fields Dashboards – II and Filter Actions |
Module 9: Python Programming
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Installing Anaconda & Basics of Python Introduction to programming languages Getting Started With Python Introduction to jupyter Notebooks Understanding what are functions Defining and calling functions Local and global variables Different types of arguments Map,reduce,filter,lambda and recursive functions Data Structures in Python Operator Input and Output Different Arithmetic , logical and Relational operators Break , continue and Pass statement List and dictionary comprehensions Understanding what are functions Defining and calling functions Local and global variables Different types of arguments Map,reduce,filter,lambda and recursive functions Different function in file handling (open,read, write,close) Different modes (r,w,a,r+,w+,a+) Exception Handling, OOPX & Regex What is exception handling Try, except, else and finally block Different types of Exception Different functions in Regex |
Module 10: Python For Data Science |
Operations Over 1-D Arrays Mathematical Operations on NumPy Mathematical Operations on NumPy II Computation Times in NumPy vs Python Lists Pandas – Rows and Columns Groupby and Aggregate Functions |
Module 11: Data Visualization Using Python- Matplotlib & Seaborn
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Introduction to Data Visualisation with Matplotlib Introduction to Matplotlib The Necessity of Data Visualisation Visualisations – Some Examples Data Visualisation: Case Study Data Handling and Cleaning: I Data Handling and Cleaning: II Outliers Analysis with Boxplots Data Visualization with Seaborn Pie – Chart and Bar Chart Revisiting Bar Graphs and Box Plots |
Module 12: Exploratory Data Analysis |
Fixing the Rows and Columns Impute/Remove Missing Values Fixing Invalid Values and Filter Data Introduction to Univariate Analysis Categorical Unordered Univariate Analysis Categorical Ordered Univariate Analysis Statistics on Numerical Features Bivariate and Multivariate Analysis Numeric – Numeric Analysis Numerical – Categorical Analysis Categorical – Categorical Analysis |
Module 13: Supervised Learning Model – Regression |
Introduction to Simple Linear Regression Introduction to Simple Linear Regression Introduction to machine learning Strength of simple linear regression Simple linear regression in python Assumptions of simple linear regression Reading and understanding the data Hypothesis testing in linear regression Residue analysis and predictions Linear Regression using SKLearn Multiple Linear Regression Motivation-when one variable is not enough Moving from SLR to MLR-new considerations Dealing with categorical variables Model assessment in comparison Multiple Linear Regression in Python Reading and understanding the data Building the model I & II Residue analysis and predictions Variable selection using RFE Industry Relevance of Linear Regression Linear regression revision Prediction versus projection Exploratory data analysis Model building – I, II & III |
Module 14: Supervised Learning Model – Classification |
Univariate Logistic Regression Finding the best fit sigmoid curve – I Finding the best fit sigmoid curve – II Multivariate Logistic Regression – Model Building Multivariate Logistic Regression – Model Building Data cleaning and preparation – I & II Building your first model Feature elimination using RFE Confusion metrics and accuracy Manual feature elimination Multivariate Logistic Regression – Model Evaluation Multivariate Logistic Regression – Model Evaluation Metrics beyond accuracy-sensitivity and specificity Sensitivity and specificity in Python Finding the optimal threshold Model evaluation metrics – exercise Logistic Regression – Industry Applications – Part I Getting familiar with logistic regression Nuances of logistic regression-sample selection Nuances of logistic regression-segmentation Nuances of logistic impression-variable transformation-I, II & III Logistic Regression: Industry Applications – Part II Model evaluation – A second look Model validation and importance of stability Tracking of model performance over time Logistic Regression – Industry Applications – Part II Commonly face challenges in implementation of logistic regression Model evaluation – A second look Model validation and importance of stability Tracking of model performance over time |
Module 15: Advanced Machine Learning |
Unsupervised Learning: Clustering Introduction to Clustering Executing K Means in Python Introduction to Business Problem Solving Case Study Demonstrationchurn example Introduction to Decision Trees Algorithms for Decision Tree Construction Hyperparameter Tuning in Decision Trees Ensembles and Random Forests Time Series Forecasting – I (BA) Introduction to Time Series Time Series Forecasting – II (BA) Introduction to AR Models Principles of Model Selection Model Building and Evaluation |
Module 16: AI- NLP, Neural Networks & Deep Learning |
History and evolution of NLP Corpus and Corpus Linguistics Introduction to the NLTK toolkit Preprocessing text data with NLTK Basic NLP tasks using NLTK (e.g., Part-ofSpeech Tagging, Named Entity Recognition) Stemming and Lemmatization Sentiment Analysis with NLTK Tokenization and Topic Modeling Bag-of-Words representation Sentiment Analysis Project: Introduction to Sentiment Analysis Sentiment Analysis using supervised and unsupervised methods Building a Sentiment Analysis model with Python Evaluating Sentiment Analysis models AI vs Deep Learning vs ML Introduction to Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) Applications of AI, ML, and DL Differences between AI, ML and DL The Concept of Neural Networks Introduction to Neural Networks Layers in Neural Networks Neural Networks – Feed-forward, Convolutional, Recurrent Feed-forward Neural Networks Convolutional Neural Networks Recurrent Neural Networks Applications of Neural Networks Building a Deep Learning model with Python Image Classification with Convolutional Neural Networks Natural Language Processing with Recurrent Neural Networks |
Data Science Projects
Customer Lifetime Value Computation:
Utilizing SQL for computing customer lifetime value, this task aids in evaluating the revenue generated by customers throughout their association with the business.
Predicting Customer Churn:
Developing a predictive model using SQL to identify customers at risk of churning by analyzing their transaction history and behavior.
E-Commerce Sales Interactive Dashboard:
Crafting an interactive dashboard with Tableau and SQL to analyze retail sales data, spot trends, and make informed decisions.
Customer Segmentation Dashboard:
Creating a customer segmentation dashboard with Tableau, categorizing customers based on demographics, behavior, and purchasing patterns.
Movie Recommendation System:
Constructing a movie recommendation system with Python, leveraging libraries like Pandas, NumPy, and Scikit-Learn to suggest movies based on user preferences and ratings.
Twitter Sentiment Analysis:
Analyzing Twitter data using Python, NLTK, and TextBlob to assess sentiment and comprehend overall sentiment on specific topics.
Visualizing COVID-19 Data:
Visualizing COVID-19 data using Python, incorporating libraries like Matplotlib, Seaborn, and Plotly to understand the pandemic’s impact across regions and countries.
Visualizing Stock Market Data:
Utilizing Python, Pandas, Matplotlib, and Bokeh to visualize stock market data, discerning stock price trends and patterns over time.
Airbnb Data Exploration:
Engaging in exploratory data analysis on Airbnb data to reveal insights into pricing, availability, and quality of Airbnb listings across diverse cities.
Bike Sharing Data Analysis:
Conducting exploratory data analysis on bike sharing data to unveil usage patterns across cities and identify influencing factors.
House Price Forecasting:
Constructing a regression model with Python and Scikit-Learn to predict house prices based on location, size, and amenities.
Credit Risk Assessment:
Building a classification model using Python and Scikit-Learn to evaluate credit risk for loan applicants, considering factors like credit history.
Sales Data Time Series Forecasting:
Creating a time series forecasting model using advanced machine learning algorithms like ARIMA and LSTM to predict future sales trends and determinants.
Product Review Sentiment Analysis:
Developing a sentiment analysis model using NLP techniques like Word Embeddings and Recurrent Neural Networks (RNN) to assess product reviews and customer sentiment.
Deep Learning-Based Segmentation:
Employing advanced deep learning methods such as Fully Convolutional Networks (FCN) and U-Net for image segmentation, identifying objects in images.
Transformer-Based Machine Translation:
Building a machine translation model using advanced deep learning techniques like Transformers to translate text between languages.
Case Studies & Assignments:
- Healthcare Customer Feedback Analysis
- Management Teams Dashboard Creation
- Retail Store Sales Report Analysis
- Software Firm Employee Data Analysis
- Industrial Data Sets Classification & Comparison
- Charts & Graphs: Frequency Distribution Table, Pie-charts, Pareto Diagram, Histogram, Scatter Plots, Heatmaps, Bar Graphs and many More.
- Patient Disease Probability Analysis Using Healthcare Data
- Car Model & Menu Item Data Combination & Configuration Probability Analysis
- Manufacturing & Product Launch Data Classification & Analysis
- Customer Complaint Resolution Analysis Using Normal Distribution Curves
- Product Rating & Employee Productivity Analysis Usign Z-Score
- New Product Need Analysis Using Hypothesis Testing
- Inventory Management & Customer Segmentation Systems Using Vlook up & Hlook Lookup
- Sales Trend & Staffing Plan Creation using Pivot Tables
- Pricing Strategy & Financial Model Creation Using What if Analysis
- Sales & Operations Dashboard Creation
- Healthcare & Construction Reporting Automation Using Macros
- Retail Sales Opportunity Analysis Using PIVOT Charts
- Accounting Firm Statement Analysis Using Sparklines & Waterfall Chart
- FMCG Marketing Spend to Sales Revenue Impact Analysis Using Regression Analysis
- Transportation Pricing Model Using Regression Analysis
Data Science Placements
At Digiperform, we uphold our commitment to nurturing your Data Science journey. Our comprehensive program not only imparts vital skills but also assures your career advancement. With our 100%* placement assistance, you can confidently embark on your Data Science path, assured that we’ll link you with prime industry prospects. We boast a robust network of hiring partners, and our devoted placement team will adeptly guide you through the process, from refining your resume to honing your interview skills. Your triumph is our utmost priority, and we’re dedicated to aiding you in securing a fulfilling position in the realm of Data Science. Join us today and step into a promising future.
Data science course fees
Course Fee: Rs. 1,22,720 for Digiperform’s comprehensive training.
Contact Information
Digiperform Corporate Office: C-30, Third Floor, Sector-2, Near Sec-15 Metro Station, Noida, Uttar Pradesh 201301, India
Email: contact@digiperform.com
Phone: +91-8527-611-500
Website: www.digiperform.com
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