One-on-One Sessions
Complimentary Study Materials
Guaranteed Job Placement
Program Overview
In modern times, the best training can provide you a better position in the technology world using a very frequent term ‘Data Science’. It has marked itself as a multi-disciplinary thing that deals with data in a structured and unstructured manner. It applies different scientific methods and mathematics to process data and takes out information from it. The at hand information trends of best data science training at Kodetree is providing around 20 percent of data in a free manner while rest 80 percent structured in a set-up for speedy analyzing. The unstructured or semi-structured details require processing to make it productive and practical for the present-day entrepreneur atmosphere. In a wide sense, this data or details are produced from a broad variety of resources such as text files, monetary logs, instruments and sensors, and multimedia forms.
Elligibility
Data Science is the newest career of the century. So, any person who is having bit knowledge about Big Data can join the said Data Science program
Easy to learn
The data science course at Kodetree is easy to learn so that no student finds it tricky to get familiar no matter what background he/she comes from.
Business process knowledge
Students can control their past domain data or academic ability to know the Business Processes executed for SAP.
100% Placement Assistance
Get 100% placement help. 350+ Industry Tie-ups to assist you with MNC level opportunities in India and internationally
Data Science Program Fee
Course Curriculum
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Training
Practical Training with Real-World projects
Curriculum
Comprehensive Curriculum Covering 90+ Modules
Extra Activities
Free Workshops on Professional Development
Job options
6-Month Internship Opportunity
Data Science Syllabus
Here’s a wide-ranging syllabus outline for Data Science training, covering the initial to advanced topics:
There is an overview of Data Science and Its Applications. Understand the roles and responsibilities of a Data Scientist. Understanding the Data Science Workflow Tools and Technologies in Data Science. Python Basics: Syntax, Data Types, and Variables. Control Structures and Functions
Study Data Cleaning and Preprocessing Techniques. Exploratory Data Analysis (EDA). Feature Engineering and Selection. Learn Descriptive and Inferential Statistics. Also the probability distributions (Normal, Binomial, Poisson, etc.). Hypothesis Testing and Confidence Intervals. Correlation and Regression Analysis
Principles of Effective Data Visualization. Visualizing Data with Tools: Tableau, Power BI, or Python Libraries. Interactive Dashboards and Reports. Supervised vs. Unsupervised Learning Regression Techniques: Linear and Logistic Regression Classification Techniques: Decision Trees, Random Forest Clustering Techniques: K-Means, Hierarchical Clustering
Support Vector Machines (SVM). Ensemble Learning (Bagging, Boosting, XGBoost). Dimensionality Reduction (PCA, t-SNE). Neural Networks Basics. Introduction to Deep Learning and Neural Networks. Working with TensorFlow and Keras. Convolution Neural Networks (CNNs) for Image Processing.
Introduction to Big Data: Hadoop and Spark. Data Processing with PySpark. Cloud Platforms: AWS, Azure, Google Cloud. Deploying Data Science Models on the Cloud. Natural Language Processing (NLP): Text Preprocessing: Tokenization, Lemmatization, and Stemming. Word Embeddings (Word2Vec, GloVe).
Introduction to Time Series Data. Forecasting Techniques: ARIMA, SARIMA, and LSTM. Seasonal Decomposition and Trend Analysis. Model Deployment and Performance Optimization. Model Evaluation Metrics: Accuracy, Precision, Recall, F1 Score. Hyperparameter Tuning: Grid Search and Random Search.
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Learn from Top Academicians & Industry Experts
He has 5+ years of experience in teaching Data science subjects. Also he has good interpersonal with excellent communication skills. He imparts training on other languages such as Python or R Programming, Advanced Statistic, Machine learning, and Big Data & Business Intelligence tools
Admission Process
Step 1
Sign up in our Data Science course
Step 2
Complete the directed assignments
Step 3
Use our placement support
Frequently Ask Questions
Machine Learning is a branch of AI where the machine learns to process the data from big data sets that are fed in the machine or software to discover and store them. These data sets are referenced by the machine to process and come back with future questions.
In Kodetree, the following skills are given to our students:
Deep perceptive of fundamental statistics / mathematics, good data modeling skills
Deep business domain data, good business analytics skills, good general idea of technical challenges and planned problem-solving skills
Good engineering skills, deep perceptive of cloud technologies, good system architecture
There is a part of the business society that can latch on to the most new trends in research. That part of the business community will take on ‘new’ things like ML. ML is a type of Artificial Intelligence, and principally means that the machine ‘learns without being taught’ as it has different statistical and algorithmic ‘facts.’ ML is currently pricey (requires the procure of specialized software or internet services, takes time and lots of effort to set up), but Kodetree can help to learn this field in a simpler way. Yes, learning ML is going to help your career. But to be helpful, you’ll need to do more than just understand what it is. Do some projects using ML.
The gains of Machine Learning are as follows,
It can simply distinguish the trends and patterns
It can learn and recover the predictions on its own and no human connection is required
It constantly improves the accurateness and efficiency
It can serve a lot of users with wide-ranging applications.
After learning the basics of machine learning, there are numerous advanced topics and areas of specialization that one can follow. Here are a few key areas to consider:
Deep Learning
Natural Language Processing (NLP)
Reinforcement Learning
Computer Vision
Model Deployment and Productionization
Ethics and Fairness in AI
Advanced Algorithms and Techniques
Big Data Technologies
MLOps
Research and Development
Completing a course on machine learning from Kodetree can open up several career options:
Machine learning engineer
Data scientist
Data analyst
Business intelligence analyst
AI research scientist
Computer vision engineer
Natural language processing (NLP) engineer
Robotics engineer
Software engineer
Big data analyst
Yes, there is 100% job placement at our institution as we have a tie-up with many MNCs.