1. Digiperform
Digiperform stands out as the leading training provider in India, specializing in digital skills. Our extensive course curriculum is intricately developed by a team of over 50 experts with significant experience in the Data Science industry. We actively solicit input and suggestions from 450 innovative businesses across Asia, ensuring that our courses remain at the forefront of industry trends.
Our curriculum is thoughtfully designed to encompass the requisite skills for roles in both small and large Data Science agencies, as well as for companies with in-house Data Science teams. At Digiperform, we prioritize a hands-on learning approach, integrating practical exercises, research, and assignments to deliver a comprehensive and practical learning experience.
Why Choose Digiperform Online Data Science Course?
Opting for Digiperform’s Online Data Science Course is a judicious choice for various reasons. Our learning platform is meticulously designed to be user-friendly and has been crafted by industry experts to facilitate a comprehensive understanding of intricate concepts.
The curriculum is thoughtfully organized, drawing insights from feedback provided by 450 businesses, ensuring its continual relevance in the dynamic digital landscape. Our pedagogical approach is practical, integrating hands-on exercises, research, and assignments to provide a well-rounded and immersive learning experience.
Whether your background is scientific, business-oriented, or non-technical, our course is customized to ensure your success. Join us to delve into the realm of data science and embark on a rewarding and enriching learning journey!
Data Science Online Course: Advantages
The landscape of data science education is expanding, with a proliferation of programs annually. While many are offered by traditional institutions, an increasing number of colleges and universities are providing data science degree programs online. The surge in online learning’s popularity is attributed to numerous advantages not typically associated with traditional classroom settings. For aspiring data scientists, opting for an online degree program presents distinct benefits:
Self-Paced Learning: Online courses afford students the flexibility to study at their preferred pace.
Convenience: Learning online allows for study sessions after work or on weekends, offering flexibility to accommodate various schedules.
Cost-Effective: Online learning eliminates commuting costs, such as gas, vehicle maintenance, and parking fees, directing savings back to the learner.
Location Independence: With only a computer and internet connection required, online courses grant the freedom to study from any location, be it at home, in a coffee shop, during vacations, or anywhere in between.
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 and Assignments
Major Projects
Customer Lifetime Value Calculation: The project involves calculating the customer lifetime value using SQL to understand the revenue generated by a customer over their lifetime.
Customer Churn Prediction: This project involves building a predictive model using SQL to identify customers who are likely to churn based on their behavior and transaction history.
Interactive Dashboard for E-Commerce Sales: The project involves creating an interactive dashboard using Tableau & SQL to analyze retail sales data, identify trends, and make data-driven decisions.
Customer Segmentation Dashboard: This project involves creating a customer segmentation dashboard using Tableau to identify customer groups based on demographics, behavior, and purchasing patterns.
Movie Recommendation System: The project involves building a movie recommendation system using Python and its libraries such as Pandas, NumPy, and Scikit-Learn. The recommendation system will suggest movies based on user preferences and ratings.
Sentiment Analysis on Twitter Data: This project involves analyzing Twitter data using Python and its libraries such as NLTK and TextBlob to perform sentiment analysis and understand the overall sentiment of a particular topic.
Visualizing COVID-19 Data: The project involves visualizing COVID-19 data using Python and its libraries such as Matplotlib, Seaborn, and Plotly to understand the impact of the pandemic on different countries and regions.
Visualizing Stock Market Data: This project involves visualizing stock market data using Python and its libraries such as Pandas, Matplotlib, and Bokeh to understand the trends and patterns in stock prices over time.
Airbnb Data Analysis: The project involves performing exploratory data analysis on Airbnb data to understand the patterns in the pricing, availability, and quality of Airbnb listings in different cities.
Bike Sharing Data Analysis: This project involves performing exploratory data analysis on bike sharing data to understand the usage patterns of bikes in different cities and identify factors that influence bike usage.
House Price Prediction: The project involves building a regression model using Python and its libraries such as Scikit-Learn to predict the prices of houses based on their features such as location, size, and amenities.
Credit Risk Prediction: This project involves building a classification model using Python and its libraries such as Scikit-Learn to predict the credit risk of loan applicants based on their credit history and other factors.
Time Series Forecasting for Sales Data: The project involves building a time series forecasting model using advanced machine learning algorithms such as ARIMA and LSTM to predict future sales trends and identify factors that influence sales.
Sentiment Analysis on Product Reviews: The project involves building a sentiment analysis model using NLP techniques such as Word Embeddings and Recurrent Neural Networks (RNN) to analyze product reviews and understand the sentiment of customers towards different products.
Segmentation using Deep Learning: This project involves using advanced deep learning techniques such as Fully Convolutional Networks (FCN) and U-Net to perform image segmentation and identify objects in images.
Machine Translation using Transformers: This project involves building a machine translation model using advanced deep learning techniques such as Transformers to translate text from one language to another.
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
100% * Placement Assistance Dedicated Placement Cell To Help You Land Your Dream Job
Data science course fees
Master Program in Data Science Fees: 1,22,720
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