Skill Centre

Data Science Training in Hyderabad-Bangalore

Batch Details

Data Science training In Hyderabad-bangalore
Trainer Name
Gopi
Trainer Experience
10 Years
Next Batch Date
21st July 24
Course Duration
60+ Classes
Call us at
+91-954 226 6111

Why Choose Skill Centre ?

Expertise and Reputation

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Flexibility and Accessibility

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Career Advancement

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Course Content and Curriculum

Ensure that the course content aligns with your learning objectives and provides comprehensive coverage of the topics you wish to learn. A good provider will offer up-to-date and relevant content that meets industry standards.

Certification and Recognition

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Student Feedback and Stories

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Qualified Instructors

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Support and Resources

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Reputation in the Industry

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Curriculum

Data Science Training in Hyderabad-Bangalore

▪ Need basic Computer Skills
▪ Basic Mathematical Concepts

▪ Work with various data generation sources
▪ Analyze structured and unstructured data
using different tools and techniques
▪ Develop an understand of Descriptive and
Predictive Analytics
▪ Apply Data-driven, Machine Learning
approaches for business decisions
▪ Build models for day-to-day applicability
▪ Perform Forecasting to take proactive
business decisions
▪ Perform Text Mining to generate Sentiment
Analysis
▪ Develop Use cases with Generative AI

▪ Getting Started with Data Science
▪ Differences and Interrelation of AI, ML, DL,
Generative AI
▪ Data Science Skill Set
▪ End to End Data Science Project Life Cycle
▪ Different types of Data Science Tasks
▪ Introduction to Big Data Analytics and its
uses
▪ Stages of Analytics - Descriptive, Predictive,
Prescriptive, etc.
▪ Course outline, road map, and takeaways
from the course
▪ Data Science Application Categories

▪ Introduction to Python Programming
▪ Installation of Python
▪ Installation of Anaconda Distribution
▪ Setting Up Python Environment
▪ Python Editors & IDEs

▪ Getting Started with Jupyter notebook
▪ Concept of Packages/Libraries
▪ Installing & loading Packages

▪ Data Types
    ▪ Integers
    ▪ Float
    ▪ String
   ▪ Boolean Numbers
    ▪ Complex Numbers
▪ Operators in Python
   ▪ Arithmetic operators
   ▪ Relational operators
   ▪ Logical operators
   ▪ Assignment operators
  ▪ Bitwise operators
  ▪ Membership operators
  ▪ Identity operators

▪ Data structures
   ▪ String Representation
  ▪ Lists
  ▪ Tuple
   ▪ Sets
   ▪ Dictionary
  ▪ Matrix
  ▪ Arrays
  ▪ Series
  ▪ Data Frames
▪ Date & Time Values
▪ Conditional Statements
    ▪ if statement
   ▪  if - else statement
   ▪ if - elif statement

    ▪ Nest if-else
   ▪ Multiple if
   ▪ Switch
▪ Loops
   ▪ While loop
   ▪ For loop
  ▪ Range()
  ▪ Iterator and generator Introduction
  ▪ For – else
   ▪ Break

▪ Functions
  ▪ Purpose of a function
   ▪ Defining a function
   ▪ Calling a function
   ▪ Function parameter passing

       i. Formal arguments
       ii. Actual arguments

      iii. Positional arguments
      iv. Keyword arguments
      v. Variable arguments
     vi. Variable keyword arguments

   vii. *args, **kwargs

▪ Function call stack
  ▪ Locals()
  ▪ Globals()
▪ Modules
   ▪ Python Code Files
    ▪ Importing functions from another file
    ▪ name: Preventing unwanted code execution
   ▪ Folders Vs Packages
   ▪ init .py
   ▪ Namespace
   ▪ Import *
   ▪ File Handling
  ▪ Exception Handling
  ▪ Oops concepts
  ▪ Classes and Objects
  ▪ Inheritance and Polymorphism
  ▪ Multi-Threading

▪ Descriptive Statistics
▪ Measures of Central Tendency
Mean/Average, Median, Mode
▪ Measures of Spread
Variance, Standard Deviation, Range
▪ Inferential Statistics
▪ Sampling
▪ Need for Sampling?
▪ Sampling Techniques
• Probability & Probability Distribution
▪ Continuous Probability Distribution /
Probability Density Function
▪ Discrete Probability Distribution /
Probability Mass Function
• Confidence interval
• Normal Distribution and Characteristics of
Normal Distribution
• Standard Normal Distribution / Z
distribution
• Z scores and the Z table
• Uniform Distribution
• F-distribution
• Binomial Distribution

• Poisson Distribution
• Bernoulli Distribution
• Chi- Square Distribution

▪ Hypothesis Testing

▪ Null and Alternative Hypothesis
▪ Type I or Alpha Error and Type II or Beta
Error
▪ Reject or acceptance criterion
▪ Confidence Level, Significance Level,
Power of Test
▪ 1 Sample t-test, 2 Sample t-test and Paired t-
test
▪ Z-test
▪ ANOVA
▪ Chi-Square test
▪ Correlation, Covariance, Associations, Odds
Ratio, Relative Risk
▪ Spurious correlation
▪ Correlation vs. Causation
▪ Data Visualization using Python
▪ Pie chart
▪ Donut Chart
▪ Histogram
▪ Density Plot
▪ Bar chart
▪ Box plot
▪ Scatter plot
▪ Scatter plot matrix
▪ Correlation Plot
▪ Line Chart
▪ Pairs Plot

▪ Data Collection
▪ Data Types namely Continuous, Discrete,
Categorical, Qualitative, Quantitative
▪ Classification of data in terms of Nominal,
Ordinal, Interval & Ratio types
▪ Batch Processing vs Real Time Processing
▪ Structured versus Unstructured vs Semi-
Structured Data
▪ Balanced versus Imbalanced datasets
▪ Big Data vs Non-Big Data

▪ Data Cleaning / Preparation - Outlier Analysis,
Missing Values Imputation
▪ Data Manipulation - Sorting, Filtering,
Duplicates, Merging, Appending, Sub setting,
Derived variables, Typecasting, Renaming,
Formatting etc.
▪ Uni variate, Bi variate, and Multivariate
Analysis
• Encoding: Dummy Variable Creation and
Label Encoding

• Scaling Techniques - Transformations,
Normalization / Standardization
• Sampling techniques for handling Balanced
vs. Imbalanced Datasets

▪ Feature Engineering on Numeric / Non-
numeric Data
▪ Feature Extraction
▪ Feature Selection

Supervised Learning
▪ Steps in Supervised Learning
▪ Difference between Regression and
Classification
▪ Training, Validation and Testing data
▪ Evaluation Strategies
• R-square, Adjusted R-square, MSE, RMSE,
MAE
• Confusion Matrix
• F-1 Score, Accuracy, Precision and Recall
• Sensitivity and Specificity
• ROC and AUC
• Hyper Parameters
• Underfit and Overfit
• Cross Validation

• Principles of Linear regression
• Assumption & Steps in Linear regression
• Simple Regression and Multiple Linear
Regression
• Variable Selection
• Gradient Descent Approach
• Ordinary least squares
• Cost Functions
• Model Development and interpretation
• Model Validation and Diagnostics
• Analysis of Regression results
• R-square, Adjusted R-square, MSE, RMSE,
MAE
• Multicollinearity (Variance Inflation Factor)
• Homoscedasticity (Equal Variance) /
Heteroscedasticity
• Advantages and Disadvantages

• Need for Logistic Regression
• Principles of Logistic Regression
• Assumption & Steps in Logistic Regression
• LOGIT link function
• Analysis of Logistic Regression results
• Confusion matrix
• False Positive, False Negative
• True Positive, True Negative
• Precision, Recall, Sensitivity, Specificity,
F1 - Score
• Receiver operating characteristics curve (ROC)
• AUC
• Advantages and Disadvantages

• Lasso Regression (L1 Regularization)
• Ridge Regression (L2 Regularization)
• Dropout (Used in Neural Networks)

• Classification and Regression Trees
• Process of Tree building
• Measures of Impurity
• Entropy, Information Gain and GINI Index
• Choosing variables for Decision nodes
• Over fitting underfitting
• Pruning – Pre and Post Prune techniques
• Generalization and Regulation Techniques
to avoid overfitting in Decision Tree
• Advantages and Disadvantages

• Bagging, Boosting, Voting, Stacking

• Random Forest and understanding various
arguments
• Checking for Underfitting and Overfitting
in Random Forest
• Generalization and Regulation Techniques
to avoid overfitting in Random Forest

• Gradient Boosting Algorithm
• Extreme Gradient Boosting (XGB) Algorithm
• Checking for Underfitting and Overfitting

• Generalization and Regulation Techniques
to avoid overfitting

 

• Deciding the K value
• Thumb rule in choosing the K value.
• Normalization of variables
• Building a KNN model by splitting the
data
• Checking for Underfitting and Overfitting
• Generalization and Regulation Techniques
to avoid overfitting

• Hyperplanes
• Maximum Margin Line
• Cost Parameters
• SVM for Noisy Data
• Non- Linear Space Classification
• Non-Linear Kernel Tricks
• Linear Kernel
• Polynomial
• Sigmoid
• Gaussian RBF
• SVM for Multi-Class Classification

• Conditional Probability
• Bayes Rule
• Naïve Bayes Classifier
• Text Classification using Naive Bayes
• Checking for Underfitting and Overfitting in
Naive Bayes
• Generalization and Regulation Techniques to
avoid overfitting in Naive Bayes

• Distance Metrics
• K means Clustering
• Hierarchical Clustering
• DBSCAN
• Clustering Evaluation metrics
• Elbow Curve / Scree Plot

• Principal Component Analysis (PCA)
• Singular Value Decomposition (SVD)

• Market Basket Analysis
• APRIORI Algorithm
• Association rules mining
• Measurement Metrics
• Support
• Confidence
• Lift

• User Based Collaborative Filtering
• Item Based Collaborative Filtering
• Similarity Metrics
• Search Based Methods

• Sources of data
• Pre-processing, corpus, DocumentTerm
Matrix (DTM) & TDM
• Tokenization, Stemming, Lemmatization,
Chunking, Lexicons, Polarity, Subjectivity
• Stop words
• Regular Expressions
• Bag of words
• Word Clouds
• Unigram, Bigram, Trigram
• Text Classification and Sentiment Analysis
• Topic Modelling

• Introduction to time series data
• Steps to forecasting
• Components to time series data
• Lag Plot
• ACF - Auto-Correlation Function /
Correlogram
• Errors in the forecast and it metrics - ME,
MAD, MSE, RMSE, MPE, MAPE
• Stationary Time Series
• Trend, Seasonality, Randomness
• Moving Averages
• Exponential Smoothing
• AR (Auto-Regressive) model for errors
• \Moving Averag                        • Exponential Smoothing          • Holt's / Double Exponential Smoothing
• Winters / Holt-Winters
• De-seasoning and de-trending
• Seasonal Indexes
• ARMA (Auto-Regressive Moving Average),
Order p and q
• ARIMA (Auto-Regressive Integrated Moving
Average), Order p, d, and q
• Multivariate Time Series Analysis (VAR -
Vector Autoregression)


Artificial Neural Networks
Introduction to Perceptron and Multilayer
Perceptron

Neurons of a Biological Brain
Artificial Neuron
Perceptron
Perceptron Algorithm
Artificial Neural Networks (ANN)
Integration functions
Activation functions (Sigmoid, Tanh,
Relu etc.)

Weights
Bias
Learning Rate - Shrinking Learning Rate,
Decay Parameters

Error functions - Entropy, Binary Cross
Entropy, Categorical Cross Entropy, KL
Divergence, etc.

Gradient Descent Algorithm
Backward Propagation
Network Topology
Principles of
Gradient Descent
(Manual
Calculation)

Learning Rate (eta)
Batch Gradient Descent
Stochastic Gradient Descent
Minibatch Stochastic Gradient Descent
Optimization Methods: Adagrad,
Adadelta, Adam

Convolution Neural Network (CNN)
Image Processing
Recurrent Neural Network
Text Analytics and Sentiment Analysis
Long Short-Term Memory (LSTM)
Gated Recurrent Network (GRU)

• Introduction
• Spark Framework
• RDD
• Pyspark

• Importance of R
• R and R-studio installation
• Getting started with R

• What is a Database
• Types of Databases
• DBMS vs RDBMS
• DBMS Architecture
• Normalization
• Install PostgreSQL
• Install MySQL
• Data Models
• DBMS Language
• ACID Properties in DBMS
• What is SQL
• SQL Data Types, commands, Operators, Keys,
Joins
• Subqueries with select, insert, update, delete
statements
• GROUP BY, HAVING, ORDER BY
• Views in SQL
• Set Operations and Types
• functions
• Triggers
• Introduction to NoSQL Concepts
• SQL vs NoSQL
• Database connection SQL to Python

•  Installation and Introduction to PowerBI
• Transforming Data using Power BI Desktop
• Importing data
• Changing Database

• Data Types in PowerBI
• Basic Transformations
• Managing Query Groups
• Splitting Columns
• Changing Data Types
• Working with Dates
• Removing and Reordering Columns
• Conditional Columns
• Custom columns
• Connecting to Files in a Folder
• Merge Queries
• Transforming Less Structured Data
• Column profiling
• Query Performance Analytics

▪ Installation and Introduction to PowerBI
▪ Workbooks
▪ Dashboards

What is Data Science ?

Data Science is an interdisciplinary field that uses techniques from statistics, computer science, and domain expertise to extract meaningful insights from structured and unstructured data. It combines data analysis, programming, and machine learning to interpret large datasets and support decision-making.

  • Data Collection: Gathering data from various sources like databases, APIs, and sensors.
  • Data Cleaning and Preparation: Handling missing values and transforming raw data into usable formats.
  • Exploratory Data Analysis (EDA): Identifying trends, patterns, and relationships in the data.
  • Machine Learning: Developing models to make predictions or automate decisions.
  • Data Visualization: Presenting findings through dashboards or reports for better understanding.
  • Data Collection: Gathering data from various sources like databases, APIs, and sensors.
  • Data Cleaning and Preparation: Handling missing values and transforming raw data into usable formats.
  • Exploratory Data Analysis (EDA): Identifying trends, patterns, and relationships in the data.
  • Machine Learning: Developing models to make predictions or automate decisions.
  • Data Visualization: Presenting findings through dashboards or reports for better understanding.
Data Science Training in Hyderabad-Bangalore
Data Science Training in Hyderabad-Bangalore
Data Science Training in Hyderabad-Bangalore
Data Science Training in Hyderabad-Bangalore

Why Data Science ?

  • Data-Driven Decision Making:

    • Businesses leverage data science to analyze trends, predict outcomes, and optimize processes. This enables more accurate and strategic decisions, reducing risks and improving efficiency.
  • Automation with Machine Learning:

    • Data science supports the development of AI-powered systems, such as recommendation engines, chatbots, and fraud detection algorithms, which automate complex tasks and improve customer experiences.
  • Competitive Advantage:

    • Organizations use data to better understand their customers, personalize offerings, and predict market trends, giving them an edge over competitors.
  • Wide Industry Applications:

    • It is relevant across multiple sectors, including healthcare (predictive analytics), finance (risk management), retail (customer segmentation), and social media (sentiment analysis).
  • Handling Big Data:

    • With the exponential growth of data, businesses require data science to make sense of vast datasets and derive actionable insights.
  • Innovation & Growth:

    • Data science drives technological innovation in fields like autonomous vehicles, personalized medicine, and smart cities, facilitating future growth and development

Prerequisites to Data Science ?

Job Opportunities

Data Science Training In Hyderabad-bangalore

Roles and Responsibilities

  • Data Analysis: Collect and analyze large datasets to extract actionable insights.
  • Model Development: Design and implement predictive models using machine learning algorithms.
  • Data Visualization: Create visual representations of data findings to communicate results effectively to stakeholders.
  • Collaboration: Work closely with cross-functional teams, including business analysts and engineers, to align data projects with business objectives.
  • Data Cleaning: Preprocess and clean data to ensure quality and usability for analysis.
  • Experimentation: Conduct A/B testing and other experimental designs to validate hypotheses.
  • Reporting: Prepare reports and presentations to share findings with management and stakeholders​

 Skills

  • Technical Skills: Proficiency in programming languages such as Python and R; strong knowledge of SQL for database management.
  • Statistical Knowledge: Understanding of statistical concepts and techniques for data analysis.
  • Machine Learning: Familiarity with machine learning libraries (e.g., Scikit-learn, TensorFlow) and algorithms.
  • Data Visualization Tools: Experience with visualization tools like Tableau, Power BI, or Matplotlib.
  • Problem-Solving: Strong analytical and critical thinking skills to interpret complex data sets.
  • Communication: Ability to present data insights clearly to both technical and non-technical audiences​​

Education:

  • Degrees: A bachelor’s degree in fields such as Computer Science, Mathematics, Statistics, or related disciplines is typically required. Many data scientists hold master’s degrees or PhDs in Data Science or Analytics.
  • Certifications: While not always necessary, certifications from recognized platforms (like Coursera or edX) in data science, machine learning, or specific tools can enhance job prospects.
  • Practical Experience: Internships, projects, and real-world applications of data science concepts

Roles and Responsibilities

  • Data Pipeline Development: Design, build, and maintain robust data pipelines to ensure efficient data flow from various sources to storage systems.
  • Data Architecture: Develop and manage the architecture of databases and data warehouses to support data retrieval and processing.
  • Data Integration: Integrate data from diverse sources, ensuring data consistency and quality across the organization.
  • Performance Optimization: Optimize data storage and retrieval processes for efficiency and speed.
  • Collaboration: Work closely with data scientists and analysts to understand their data needs and ensure that appropriate data is available for analysis.
  • Monitoring and Maintenance: Continuously monitor and maintain data systems to ensure reliability and performance​

Skills

  • Programming Proficiency: Strong knowledge of programming languages such as Python, Java, or Scala.
  • Database Management: Expertise in SQL and experience with database technologies like MySQL, PostgreSQL, or NoSQL databases (e.g., MongoDB).
  • Big Data Technologies: Familiarity with tools like Apache Hadoop, Spark, and Kafka for processing large datasets.
  • Data Warehousing: Understanding of data warehousing solutions like Amazon Redshift or Google BigQuery.
  • Cloud Computing: Experience with cloud platforms like AWS, Azure, or Google Cloud for data storage and processing.
  • Data Modeling: Ability to design data models that meet the needs of business intelligence and analytics​

Education :

  • Degrees: A bachelor’s degree in Computer Science, Information Technology, or a related field is generally required. Many data engineers hold master’s degrees in Data Engineering or Data Science.

Roles and Responsibilities

  • Model Development: Design, implement, and optimize machine learning models to solve specific business problems.
  • Data Preparation: Work on data cleaning, transformation, and feature selection to prepare datasets for training.
  • Model Evaluation: Assess model performance using various metrics and conduct experiments to improve accuracy.
  • Deployment: Deploy machine learning models into production environments, ensuring they integrate smoothly with existing systems.
  • Monitoring and Maintenance: Continuously monitor models for performance degradation and retrain them as necessary to maintain accuracy.
  • Collaboration: Work closely with data scientists, software engineers, and product teams to align machine learning solutions with business needs​

 Skills

  • Programming Languages: Proficiency in languages such as Python, Java, or Scala for developing machine learning algorithms.
  • Machine Learning Frameworks: Familiarity with frameworks like TensorFlow, PyTorch, or Scikit-learn for building models.
  • Mathematics and Statistics: Strong foundation in mathematical concepts, especially linear algebra, calculus, and statistics.
  • Data Handling: Skills in SQL and experience with data processing libraries like Pandas and NumPy.
  • Cloud Platforms: Knowledge of cloud services (e.g., AWS, Azure, Google Cloud) for deploying machine learning models.
  • Software Development Practices: Understanding of version control (e.g., Git), CI/CD pipelines, and containerization.​
     

Education:

  • Degrees: A bachelor’s degree in Computer Science, Mathematics, Statistics, or a related field is typically required. Many ML engineers have master’s degrees or higher in Data Science or Machine Learning

Roles and Responsibilities

  • Data Analysis: Collect, analyze, and interpret complex datasets to identify trends and patterns that can inform business decisions.
  • Report Generation: Develop and maintain reports and dashboards using BI tools (e.g., Tableau, Power BI) to present insights effectively.
  • KPI Monitoring: Track key performance indicators (KPIs) to assess business performance and recommend areas for improvement.
  • Collaboration: Work closely with various departments (e.g., finance, marketing) to understand their data needs and provide actionable insights.
  • ata Governance: Ensure data accuracy, integrity, and security throughout the reporting and analysis process​​
     

Skills

  • Technical Proficiency: Expertise in BI tools like Tableau, Power BI, or Qlik for data visualization and reporting.
  • SQL Skills: Strong knowledge of SQL for data extraction, manipulation, and analysis from databases.
  • Analytical Skills: Ability to analyze large datasets and derive meaningful insights to support decision-making.
  • Communication Skills: Proficient in translating complex data findings into clear, actionable recommendations for stakeholders.
  • Business Acumen: Understanding of business operations and strategies to align data insights with organizational goals​

Education:

  • Degrees: A bachelor’s degree in fields such as Business Administration, Information Technology, Computer Science, or a related discipline is typically required. Many BI analysts hold master’s degrees in Data Analytics or Business Intelligence.

Roles and Responsibilities

  • Data Collection: Design experiments and surveys to collect data accurately and efficiently.
  • Data Analysis: Apply statistical methods to analyze data and interpret results, drawing meaningful conclusions.
  • Statistical Modeling: Develop and implement statistical models to understand relationships within data and predict future trends.
  • Reporting Findings: Communicate statistical findings through reports, presentations, and visualizations to non-technical stakeholders.
  • Collaboration: Work with researchers, data scientists, and business leaders to address specific questions and ensure the appropriate application of statistical methods​​
     Skills
  • Statistical Knowledge: Strong foundation in statistical theories and methodologies, including probability, regression analysis, and hypothesis testing.
  • Programming Skills: Proficiency in statistical programming languages such as R or Python for data analysis and modeling.
  • Data Visualization: Ability to use data visualization tools (e.g., Tableau, ggplot2) to present data findings clearly and effectively.
  • Analytical Skills: Strong analytical and critical thinking skills to interpret complex datasets and draw valid conclusions.
  • Communication: Excellent verbal and written communication skills to explain statistical concepts and findings to diverse audiences​

Education:

  • Degrees: A bachelor’s degree in Statistics, Mathematics, or a related field is typically required. Many statisticians hold master’s degrees or PhDs in Statistics or Biostatistics.

Roles and Responsibilities

  • Data Modeling: Design and implement data models that support the organization’s data needs, ensuring data is structured appropriately for analysis and reporting.
  • Database Design: Develop and manage databases to ensure data storage is efficient and meets performance requirements.
  • Data Integration: Oversee the integration of various data sources, ensuring data consistency and accessibility across the organization.
  • Data Governance: Establish and enforce data governance policies to ensure data integrity, security, and compliance with regulations.
  • Collaboration: Work with IT, data scientists, and business stakeholders to align data architecture with organizational goals and needs​​

 Skills

  • Technical Proficiency: Expertise in database management systems (e.g., Oracle, SQL Server, MongoDB) and data warehousing solutions (e.g., Amazon Redshift, Google BigQuery).
  • Data Modeling Techniques: Strong understanding of data modeling techniques and tools (e.g., ERwin, IBM InfoSphere Data Architect).
  • Programming Skills: Familiarity with programming languages such as SQL, Python, or Java for data manipulation and automation.
  • Big Data Technologies: Knowledge of big data technologies (e.g., Hadoop, Spark) to manage large volumes of data.
  • Communication Skills: Ability to communicate complex data concepts clearly to both technical and non-technical stakeholders​​

Education:

  • Degrees: A bachelor’s degree in Computer Science, Information Systems, or a related field is typically required. Many data architects hold advanced degrees in Data Science or Information Technology.

Data Analytics Testing Certification

Choose a Certification Program

  • Some popular certifications include:
    • IBM Data Science Professional Certificate
    • Google Data Analytics Professional Certificate
    • Microsoft Certified: Azure Data Scientist Associate
    • Certified Analytics Professional (CAP)
    • Data Science MicroMasters from MIT

Review Prerequisites

  • Check the prerequisites for the chosen certification. Some may require a background in statistics, programming, or specific tools (e.g., Python, R, SQL).
  • Many programs are designed for beginners, while others may target more experienced professionals.

Enroll in Courses

  • Most certifications involve enrolling in a series of online courses. Platforms like Coursera, edX, and Udacity offer structured learning paths with lectures, assignments, and projects.
  • For example, the IBM Data Science Professional Certificate consists of multiple courses that cover essential topics in data science.

Complete Coursework and Assignments

  • Engage with the coursework, which often includes video lectures, readings, quizzes, and practical assignments.
  • Hands-on projects are a critical component, allowing you to apply what you’ve learned to real-world scenarios.

Prepare for the Exam

  • If the certification includes a formal examination, prepare thoroughly. Review the exam format, study materials, and practice exams, if available.
  • Some programs may also offer forums or study groups to connect with other learners.
Take the Exam
  • Schedule and take the certification exam (if applicable). This could be an online proctored exam or a project submission, depending on the certification.
  • Ensure you meet any technical requirements for online exams (e.g., software installation, camera setup).
Receive Certification
  • Upon successfully completing all requirements (courses, assignments, exams), you will receive your certification.
  • Certificates can usually be shared on professional networks like LinkedIn, enhancing your resume.
Continued Learning
  • Some certifications may require renewal or continued education to maintain the credential. Stay updated with new trends and technologies in data science.
Additional Resources

Course Outcome ?

Data Science Training In Hyderabad-bangalore

  • Gain foundational knowledge of data science concepts and techniques.
  • Develop proficiency in programming languages such as Python and R for data analysis.
  • Understand the data science workflow, including data collection, cleaning, and preprocessing.
  • Apply statistical analysis and modeling techniques to draw insights from data.
  • Utilize machine learning algorithms for predictive modeling and classification tasks.
  • Create data visualizations to effectively communicate findings and insights.
  • Master data manipulation and querying using SQL and NoSQL databases.
  • Conduct exploratory data analysis (EDA) to uncover patterns and trends in datasets.
  • Implement best practices for data governance, security, and ethical considerations in data handling.
  • Collaborate on real-world projects to apply data science skills in a practical context.

Mode Of Trainings

Data Science training In Hyderabad-bangalore

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General FAQs

Data Science Training In Hyderabad-bangalore

Generally, a basic understanding of statistics and programming (preferably Python or R) is recommended. Some programs may require knowledge of mathematics or data analysis.

 

Topics often include data manipulation, machine learning, statistical analysis, data visualization, data engineering, and project work.

Python and R are the most commonly used languages, with SQL also being essential for data manipulation.

Course durations vary; they can range from a few weeks for intensive boot camps to several months for comprehensive programs.

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Yes, most Data Science courses include practical projects to apply learned concepts to real-world scenarios.

Yes, most institutes offer a certificate upon successful completion of the course, which can enhance your resume.

Many institutes offer online options, allowing flexibility to learn at your own pace.

Graduates can pursue various roles such as Data Scientist, Data Analyst, Machine Learning Engineer, and Business Intelligence Analyst.

Not always; many courses cater to beginners, while others may have advanced tracks for experienced professionals.

Assessment methods can include quizzes, assignments, projects, and final exams.