Data Science Course Overview
Discover the exciting world of Data Science through our comprehensive online training program, designed to equip you with practical skills and real-world examples. Led by industry experts, our course covers key concepts using Hadoop, R programming, and machine learning, offering valuable insights into big data analysis, R analytical tools, and dataset examination.
Whether you prefer live classes with engaging videos or a more flexible option without videos, our expert instructors will guide you through the essential skills needed in data transformation, visualization, and exploratory analysis. This user-friendly course is tailored for beginners, focusing on practical applications of Data Science, ensuring you develop the foundational knowledge and confidence to excel in this rapidly evolving field. Start your journey today and unlock the potential of Data Science.
Description
Data science is like a detective investigating a massive pile of information to uncover hidden gems of knowledge. It’s all about using fancy tools and brainpower to make sense of data, whether it’s numbers, words, images, or anything else. By crunching numbers and running clever algorithms, data scientists can reveal patterns, trends, and insights that help businesses and organizations make smarter decisions. It’s basically turning data chaos into useful information gold!
Course Goals
By completing the Data Science Online Course at Techno Excel, you’ll master a variety of skills crucial for success in the field:
- Solid grasp of Data Science: You’ll gain a thorough understanding of Data Science principles, laying a strong foundation for your career journey.
- Proficiency in handling big data: Learn how to effectively analyze and manage large datasets, a vital skill in today’s data-driven world.
- Expertise in Data Mining: Explore techniques and tools for extracting valuable insights from complex datasets, enhancing your ability to derive meaningful conclusions.
- Statistical know-how: Develop your skills in statistics, enabling you to make informed decisions and interpretations based on data analysis.
- Utilization of essential tools: Acquire proficiency in using popular tools like Tableau and MapReduce, empowering you to efficiently handle and visualize data.
- Decision tree creation: Learn how to construct decision trees, a valuable technique for modeling and analyzing complex decision-making processes.
- Exploration of Big Data concepts: Dive into the intricacies of Big Data, understanding its impact, challenges, and potential applications in various domains.
This course is designed to provide you with the knowledge and practical skills needed to thrive in the dynamic field of Data Science. Whether you’re delving into data analysis or employing advanced tools for strategic decision-making, this program will equip you with the expertise to excel.
Prerequisites/ Who is this course for?
If you’ve got a grasp on some basics, you’re in! Whether you’re in IT or just tech-curious, as long as you’ve got a handle on:
· Math (nothing too fancy!)
· Stats (just the basics)
· Any programming language (even if it’s just a smidge)
You’re good to go! No need for advanced knowledge, we’re keeping it accessible for anyone with a solid foundation in these areas. Ready to dive in?
- IT Professionals Looking to Level Up: Whether you’re aiming to excel in Development or Data Science, this course is tailor-made for IT pros eager to advance their careers.
- College Students of Various Streams: If you’re studying B.E, B.Tech, BSC, MCA, M.Sc Computers, M.Tech, BCA, or BCom, regardless of your specialization, this course offers valuable insights to complement your academic journey.
- Fresh Graduates Eager to Enhance Skills: Just stepped out of university and want to stand out in the competitive job market? This course equips you with practical skills and knowledge to kickstart your career on the right note.
- Open to All Skill Levels: Whether you’re a seasoned IT veteran or just starting out, this course caters to a diverse audience. It’s designed to benefit anyone seeking to broaden their expertise in the IT domain.
Don’t miss out on this enriching learning journey – join us at Techno Excel for a rewarding experience in mastering Data Science Online Training programming!
FLAT 20% ON THE COURSE FREE
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45 Days
Course Duration
Introductory
For beginners
Course Delivery
Classroom or Online
Course Structure
Session 1: Basic statistical concepts : This session introduces you with how statistics is used in business with basic statistical concepts like levels of data and measures of central tendencies.
- Measures of central tendencies
- Measures of variability
- Measures of shape
- Introduction to probability
Session 2: Inferential Statistics : You will start making statistical inferences about populations from samples
- Sampling
- Estimating the population Mean Using Z-statistic and T- statistic
- Hypothesis testing
- Confidence Intervals
Session 1: Basic concepts of R programming
This session covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, organizing, and commenting R code.
Session 2: Continuation of R concepts and execute statistical concepts using R
- Pre-processing techniques: Binning, Filling missing values, Standardization & Normalization, type conversions, train-test data split, ROCR1
- Other R concept
- Exploratory Data Analysis
Session 3: Introduction to ML Algorithms : Preparing data as an input for machine learning algorithms
- Case Study
- Assignment on R understandings
Session 4: Execute all machine algorithms in R
Session 1: Linear Regression
- Simple Linear Regression
- Coefficient of determination
- Significance Tests
- Residual Analysis
- Confidence & Prediction intervals
- Multiple linear regression
- Coefficient of Determination
- Interpretation of regression coefficients
- Categorical variables in regression
- Heteroscedasticity, Multi-co linearity outliers
- R-square and goodness of fit
- Hypothesis testing of Regression Model
- Transformation of variables
- Polynomial Regression
Followed by a Case Study
Session 2: Logistic Regression
Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.
- Logistic function
- Estimation of probability using logistic regression
- Model Evaluation
- Confusion Matrix
Followed by a Case Study
Session 3: Time series data : The focus is on analyzing and understanding Time Series with financial markets as the case study
- Trend Analysis
- Cyclical & Seasonal Analysis
- Smoothing; Moving Averages
- Auto-Correlation
- ARIMA; ARIMAX
- Applications of Time Series in Financial Markets
Followed by a Case Study
- Session 1: Clustering
- What is Clustering
- Clustering examples in Business Verticals
- Solution Strategies for Clustering
- Finding pattern and Fixed Pattern Approach
- Limitations of Fixed Pattern Approach
- Machine Learning Approaches for Clustering
- Iterative based K-Means & K-Medoid Approaches
- Hierarchical Agglomerative Approaches
- Density based DB-SCAN Approach
- Evaluation Metrics for Clustering
- Cohesion, Coupling Metric
- Correlation Metric
Followed by a Case Study
Session 2: Decision Tree (In Python & R)
- Introduction
- Building a Decision Tree
- Entropy, Information Gain
- Regression using Decision Tress
- Bias- Variance trade off
- Limitations
Followed by a Case Study
Session 3: Support Vector machines (In Python)
- Loss function based interpretation
- Linear svm
- Non linear svm and kernel function
Followed by a Case Study
Session 4: KNN (In Python & R)
- KNN learning
- Limitation
- KNN regression
- Applying KNN and parameter tuning
Session 5: Neural Networks (In Python)
- Introduction
- Perceptrons
- Self organizing maps
- Auto encoders
- Back propagation and typical feed forward algorithm
- Vanishing gradient problem
Followed by a Case Study
Session 6: Association Rules (In R)
- Apriori Model
- Pros and cons of the model
- Recommemder Systems
- User-user
- Item-item
- Content based
Followed by a Case Study
Session 7:Feature Engineering (In Python & R)
- Dimensionality Reduction
- PCA and EDA
- Eigen values and eigen vectors
Know your Trainer
Nitin Singh is an analytics professional having around 7 years of experience in data science and machine learning. He has worked with companies like Amazon and Deloitte in their core analytics wing. Currently he is working with Prime hospitals (a US based hospital chain) in building advance healthcare solutions using machine learning and A.I. Academically, he has completed his Bachelors in Engineering from Osmania university in 2011 and was part of the founding batch of business analytics program from the Indian school of business in 2013-14. He is currently pursuing a 4-month advance course in deep learning and AI from fellowship.ai under the guidance of top data scientists across the globe.Lets connect
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Today the most precious thing on the world is no more oil but data and here at TechnoExcel we try to leverage it and use it at its full potential.
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