DATA ANALYSIS & SCIENCE

The field of data analysis and science has become increasingly crucial in today’s data-driven world. As organizations across various industries continue to accumulate vast amounts of data, the ability to extract meaningful insights from this information has become a valuable skill. A comprehensive course in data analysis and science equips individuals with the knowledge and tools necessary to navigate the complexities of data and derive actionable insights.

Level

Certificate 

Mode of Training

Offline / Online

Course Duration

6 months 

Job Assistant

100% Job Assistant

ELIGIBILITY

Open to all students. No experience required, and no skill background is needed to thrive in this course.

CERTIFICATION

Upon successful completion of the course, participants will receive a recognized certification, validating their proficiency in Data Analysis & Data Science. This certification serves as a valuable asset in career advancement and job opportunities in the rapidly evolving digital landscape.the graphic designing course is so beneficial for all the students

ASSESSMENTS

  • Periodic evaluations are integrated into the course duration.
  • Assessments encompass quizzes, assignments, projects, case studies, and objective/subjective evaluations.
  • These evaluations foster consistent student engagement, promoting active learning.
  • Successful completion of evaluations, meeting attendance criteria, end Graphic designing course assessment, and project work lead to a certificate of completion/participation.

About The Course

Certainly! Data analysis and data science courses typically cover a range of topics related to collecting, processing, analyzing, and interpreting data to extract valuable insights. These courses are designed to equip individuals with the skills needed to work with large datasets and make informed decisions based on data. Here are some key aspects you might find in a data analysis and science course:

COURSE DETAILS

DATA ANALYTICS & SCIENCE

1.STATISTICAL CONCEPTS

Descriptive Statistics,Inferential Statistics,Experimental Design,Sampling Techniques,Statistical Software,Data Visualization,Bayesian Statistics,Time Series Analysis,Statistical Ethics

2.DATA VISUALIZATION

Types of Visualizations,Tools and Software,Principles of Effective Data Visualization,Interactive Data Visualizations,Storytelling with Data,Data Visualization Best Practices ,Geospatial Data Visualization,Data Visualization for Big Data,Ethical Considerations

3.PYTHON

Web Development,Data Science and Analytics,Machine Learning and Artificial Intelligence,Automation and Scripting,Content Management Systems (CMS) Text Processing and Natural Language Processing (NLP),Documentation,Graphics and Visualization,Interactive Dashboards,Content Scraping,Social Media and API Integration, Collaboration and Version Control,Educational Content

4.CLEANING AND PROCESSING DATA

Data Cleaning [Handling Missing Values,Outlier Detection,Data Validation,Duplicate Removal], Data Transformation,Data Imputation[Handling MissingData],Data Reduction,Data Normalization and Standardization,Time Series Data Preprocessing,Text Data Processing,Data Quality Checks, Handling Skewed Data,Data Security and Privacy,Data Versioning,Data Documentation,Data Validation and Cross-Checking

5.STATISTICAL INFERENCE

Population and Sample,Sampling Distributions,Point Estimation,Confidence Intervals,Hypothesis Testing,Pvalues,Comparing Two Samples,ANOVA (Analysis of Variance),Regression Analysis, Non-parametric Tests,Bayesian Inference,Bootstrapping,Statistical Power,Residual Analysis,Interpretation of Results

6.EXPLORATORY DATA ANALYSIS

Descriptive Statistics,Univariate Analysis,Bivariate Analysis,Multivariate Analysis,Outlier Detection,Missing Values Analysis,Data Distribution,Feature Engineering,DataVisualization Tools,Statistical Tests for EDA, Data Exploration in Time Series Data,Interactive Dashboards,Geospatial Data Exploration,Dimensionality Reduction,Summary and Documentation,

7.MACHINE LEARNING

Supervised Learning, Unsupervised Learning ,Types of Algorithms ,Model Evaluation, Hyper parameter Tuning, Over fitting and Under fitting ,Feature Engineering, Ensemble Learning, Reinforcement Learning ,Natural Language Processing (NLP), Computer Vision, Time Series Analysis ,Deployment andScaling ,Explain ability and Inter pretability ,Ethical Considerations

8.PREDICTINE ANALYSTICS

Predictive Modeling, Machine Learning Algorithms, Feature Selection and Engineering Model Evaluation and Validation Hyper parameter Tuning, Assembly Methods, Classification Models, Regression Models ,Time Series Analysis, Text Analytics, Predictive Analytics in Business and Healthcare Predictive Analytics, Fraud Detection Predictive Maintenance, and Ethical Considerations

9.PRACTICAL APPLICATIONS INVARIOUS DOMAIN

Healthcare ,Finance, Retail ,Manufacturing ,Education, Transportation and Logistics ,Telecommunications, Energy ,Agriculture ,Marketing, Human Resources ,Real Estate, Environmental Monitoring ,Government and Public Services ,Entertainment,

How to Apply

  • Admission Form: Form duly filled and signed ( available at admission Counter )
  • Qualification Proof: Self attested photocopy of last qualifying Mark Sheet
  • Photo: Two latest passport size color photographs
  • Fee Submission: Fees can be paid through Cash or Online Transfer
  • EMIs: Fee can be paid in Monthly EMI

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