One-on-One Sessions
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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.
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
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