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Curriculum in Data Science

Data Science Basics
  • Introduction to Data Science
  • Significance of Data Science
  • R Programming basics
  • Python Fundamentals
  • Python Introduction
  • Indentations in Python
  • Python data types and operators
  • Python Functions
  • Data Structures and Data Manipulation
  • Data Structures Overview
  • Identifying the Data Structures
  • Allocating values to the Data Structures
  • Data Manipulation Significance
  • Dplyr Package and performing different data manipulation operations
  • Data visualization
  • Introduction to Data Visualisation
  • Various kinds of graphs, Graphics grammar
  • Ggplot2 package
  • Multivariant analysis by using geom_boxplot
  • Univariant analysis
  • Histogram, barplot, multivariate distribution, and density plot
  • Bar plots for the categorical variables through geop_bar() and the theme() layer
  • Statistics
  • Statistics Importance
  • Statistics classification, Statistical terminology
  • Data types, Probability types, measures of speed, and central tendency
  • Covariance and Correlation, Binary and Normal distribution
  • Data Sampling, Confidence, and Significance levels
  • Hypothesis Test and Parametric testing
  • Introduction to Machine Learning
  • Machine Learning Fundamentals
  • Supervised Learning, Classification in Supervised Learning
  • Linear Regression and mathematical concepts related to linear regression
  • Classification Algorithms, Ensemble Learning techniques
  • Logistic Regression
  • Logistic Regression Introduction
  • Logistic vs Linear Regression, Poisson Regression
  • Bivariate Logistic Regression, math related to logistic regression
  • Multivariate Logistic Regression
  • Building Logistic Models
  • False and true positive rate
  • Real-time applications of Logistic Regression
  • Random Forest and Decision Trees
  • Classification Techniques
  • Decision Tree Induction Algorithm
  • Implementation of Random Forest in R
  • Differences between classification tree and regression tree
  • Naive Bayes, SVM
  • Entropy, Gini Index, Information Gain
  • Unsupervised learning
  • Clustering, K-means clustering, Canopy Clustering, and Hierarchical Clustering
  • Unsupervised learning, Clustering algorithm, K-means clustering algorithm
  • K-means theoretical concepts, k-means process flow, and K-means implementation
  • Implementing Historical Clustering in R
  • PCA(Principal Component Analysis) Implementation in R
  • Denial-of-Service
  • DoS/DDoS Concepts
  • Botnets
  • DoS/DDoS Attack Techniques
  • DDoS Case Study
  • DoS/DDoS Countermeasures
  • Natural Language Processing
  • Natural language processing and Text mining basics
  • Significance and use-cases of text mining
  • NPL working with text mining, Language Toolkit(NLTK)
  • Text Mining: pre-processing, text-classification and cleaning
  • Mathematics for Data Science
  • Numpy Basics
  • Numpy Mathematical Functions
  • Probability Basics and Notation
  • Correlation and Regression
  • Joint Probabilities
  • Bayes Theorem
  • Conditional Probability, sum rule, and product rule
  • Scientific Computing through Scipy
  • Scipy Introduction and characteristics
  • Integrate, Cluster, Signal, Fftpack, and Bayes Theorem
  • Python Integration with Spark
  • Pyspark basics
  • Uses and Need of pyspark
  • Pyspark installation
  • Advantages of pyspark over MapReduce
  • Pyspark applications
  • Deep Learning and Artificial Intelligence
  • Machine Learning effect on Artificial Intelligence
  • Deep Learning Basics, Working of Deep Learning
  • Regression and Classification in the Supervised Learning
  • Association and Clustering in unsupervised learning
  • Basics of Artificial Intelligence and Neural Networks
  • Supervised Learning in Neural Networks, multi-layer network
  • Deep Neural Networks, Convolutional Neural Networks
  • Reinforcement Learning
  • Recurrent Neural Networks, Deep learning graphics processing unit
  • Deep Learning Applications, Time series modeling
  • Keras and TensorFlow API
  • Tensorflow Basics and Tensorflow open-source libraries
  • Deep Learning Models and Tensor Processing Unit(TPU)
  • Graph Visualisation, keras
  • Keras neural-network
  • Define and Composing multi-complex output models through Keras
  • Batch normalization, Functional and Sequential composition
  • Implementing Keras with tensorboard
  • Implementing neural networks through TensorFlow API
  • Restricted Boltzmann Machine and Autoencoders
  • Basics of Autoencoders and rbm
  • Implementing RBM for the deep neural networks
  • Autoencoders features and applications
  • Big Data Hadoop and Spark
  • Big Data and Hadoop Basics
  • Hadoop Architecture, HDFS
  • MapReduce Framework and Pig
  • Hive and HBase
  • Basics of Scala and Functional Programming
  • Kafka basics, Kafka Architecture
  • Kafka cluster and Integrating Kafka with Flume
  • Introduction to Spark
  • Spark RDD Operations, writing spark programs
  • Spark Transformations, Spark streaming introduction
  • Spark streaming Architecture, Spark Streaming Features
  • Structured streaming Architecture, Dstreams, and Spark Graphx
  • Tableau
  • Data Visualisation Basics
  • Data Visualisation Applications
  • Tableau Installation and Interface
  • Tableau Data Types, Data Preparation
  • Tableau Architecture
  • Getting Started with Tableau
  • Creating sets, Metadata and Data Blending
  • Arranging visual and data analytics
  • Mapping, Expressions, and Calculations
  • Parameters and Tableau prep
  • Stories, Dashboards, and Filters
  • Graphs, charts
  • Integrating Tableau with Hadoop and R
  • MongoDB
  • MongoDB and NoSQL Basics
  • MongoDB Installation
  • Significance of NoSQL
  • CRUD Operations
  • Data Modeling and Management
  • Data Indexing and Administration
  • Data Aggregation Schema
  • MongoDB Security
  • Collaborating with Unstructured Data
  • SAS Basics
  • SAS Enterprise Guide
  • SAS functions and Operators
  • SAS Data Sets compilation and creation
  • SAS Procedures
  • SAS Graphs
  • SAS Macros
  • PROC SQL
  • Advance SAS
  • MS Excel
  • Entering Data
  • Logical Functions
  • Conditional Formatting
  • Validation, Excel formulas
  • Data sorting, Data Filtering, Pivot Tables
  • Creating charts, Charting techniques
  • File and Data security in excel
  • VBA macros, VBA IF condition, and VBA loops
  • VBA IF condition, For loop
  • VBA Debugging and Messaging
  • Curriculum in Data Science

    Data science is preferred by more than 55% of developers. In the tech industry, data science is the most well-liked and in-demand programming language.

    • Introduction to Data Science
    • Significance of Data Science
    • R Programming basics
    • Python Introduction
    • Indentations in Python
    • Python data types and operators
    • Python Functions
    • Data Structures Overview
    • Identifying the Data Structures
    • Allocating values to the Data Structures
    • Data Manipulation Significance
    • Dplyr Package and performing different data manipulation operations
    • Introduction to Data Visualisation
    • Various kinds of graphs, Graphics grammar
    • Ggplot2 package
    • Multivariant analysis by using geom_boxplot
    • Univariant analysis
    • Histogram, barplot, multivariate distribution, and density plot
    • Bar plots for the categorical variables through geop_bar() and the theme() layer
    • Statistics Importance
    • Statistics classification, Statistical terminology
    • Data types, Probability types, measures of speed, and central tendency
    • Covariance and Correlation, Binary and Normal distribution
    • Data Sampling, Confidence, and Significance levels
    • Hypothesis Test and Parametric testing
    • Machine Learning Fundamentals
    • Supervised Learning, Classification in Supervised Learning
    • Linear Regression and mathematical concepts related to linear regression
    • Classification Algorithms, Ensemble Learning techniques
    • Logistic Regression Introduction
    • Logistic vs Linear Regression, Poisson Regression
    • Bivariate Logistic Regression, math related to logistic regression
    • Multivariate Logistic Regression
    • Building Logistic Models
    • False and true positive rate
    • Real-time applications of Logistic Regression
    • Classification Techniques
    • Decision Tree Induction Algorithm
    • Implementation of Random Forest in R
    • Differences between classification tree and regression tree
    • Naive Bayes, SVM
    • Entropy, Gini Index, Information Gain
    • Clustering, K-means clustering, Canopy Clustering, and Hierarchical Clustering
    • Unsupervised learning, Clustering algorithm, K-means clustering algorithm
    • K-means theoretical concepts, k-means process flow, and K-means implementation
    • Implementing Historical Clustering in R
    • PCA(Principal Component Analysis) Implementation in R
    • DoS/DDoS Concepts
    • Botnets
    • DoS/DDoS Attack Techniques
    • DDoS Case Study
    • DoS/DDoS Countermeasures
    • Natural language processing and Text mining basics
    • Significance and use-cases of text mining
    • NPL working with text mining, Language Toolkit(NLTK)
    • Text Mining: pre-processing, text-classification and cleaning
    • Numpy Basics
    • Numpy Mathematical Functions
    • Probability Basics and Notation
    • Correlation and Regression
    • Joint Probabilities
    • Bayes Theorem
    • Conditional Probability, sum rule, and product rule
    • Scipy Introduction and characteristics
    • Integrate, Cluster, Signal, Fftpack, and Bayes Theorem
    • Pyspark basics
    • Uses and Need of pyspark
    • Pyspark installation
    • Advantages of pyspark over MapReduce
    • Pyspark applications
    • Machine Learning effect on Artificial Intelligence
    • Deep Learning Basics, Working of Deep Learning
    • Regression and Classification in the Supervised Learning
    • Association and Clustering in unsupervised learning
    • Basics of Artificial Intelligence and Neural Networks
    • Supervised Learning in Neural Networks, multi-layer network
    • Deep Neural Networks, Convolutional Neural Networks
    • Reinforcement Learning
    • Recurrent Neural Networks, Deep learning graphics processing unit
    • Deep Learning Applications, Time series modeling
    • Tensorflow Basics and Tensorflow open-source libraries
    • Deep Learning Models and Tensor Processing Unit(TPU)
    • Graph Visualisation, keras
    • Keras neural-network
    • Define and Composing multi-complex output models through Keras
    • Batch normalization, Functional and Sequential composition
    • Implementing Keras with tensorboard
    • Implementing neural networks through TensorFlow API
    • Basics of Autoencoders and rbm
    • Implementing RBM for the deep neural networks
    • Autoencoders features and applications
    • Big Data and Hadoop Basics
    • Hadoop Architecture, HDFS
    • MapReduce Framework and Pig
    • Hive and HBase
    • Basics of Scala and Functional Programming
    • Kafka basics, Kafka Architecture
    • Kafka cluster and Integrating Kafka with Flume
    • Introduction to Spark
    • Spark RDD Operations, writing spark programs
    • Spark Transformations, Spark streaming introduction
    • Spark streaming Architecture, Spark Streaming Features
    • Structured streaming Architecture, Dstreams, and Spark Graphx
    • Data Visualisation Basics
    • Data Visualisation Applications
    • Tableau Installation and Interface
    • Tableau Data Types, Data Preparation
    • Tableau Architecture
    • Getting Started with Tableau
    • Creating sets, Metadata and Data Blending
    • Arranging visual and data analytics
    • Mapping, Expressions, and Calculations
    • Parameters and Tableau prep
    • Stories, Dashboards, and Filters
    • Graphs, charts
    • Integrating Tableau with Hadoop and R
    • MongoDB and NoSQL Basics
    • MongoDB Installation
    • Significance of NoSQL
    • CRUD Operations
    • Data Modeling and Management
    • Data Indexing and Administration
    • Data Aggregation Schema
    • MongoDB Security
    • Collaborating with Unstructured Data
    • SAS Enterprise Guide
    • SAS functions and Operators
    • SAS Data Sets compilation and creation
    • SAS Procedures
    • SAS Graphs
    • SAS Macros
    • PROC SQL
    • Advance SAS
    • Entering Data
    • Logical Functions
    • Conditional Formatting
    • Validation, Excel formulas
    • Data sorting, Data Filtering, Pivot Tables
    • Creating charts, Charting techniques
    • File and Data security in excel
    • VBA macros, VBA IF condition, and VBA loops
    • VBA IF condition, For loop
    • VBA Debugging and Messaging

    Data Science Training Projects

    Useful and engaging projects to develop your data science skills.

     

    Dataset Exploratory Data Analysis (EDA)

    To get a sense of a dataset, start with simple data visualization and summary statistics.

     

    Linear Regression for Predictive Modeling

    Predict a target variable based on one or more features by using a straightforward linear regression model.

     

    Classification with the Iris Dataset

    Classify iris blossoms using techniques like Logistic Regression, Decision Trees, or K-Nearest Neighbors.

     

    Image Classification with CNN

    Use a dataset like CIFAR-10 to construct a Convolutional Neural Network (CNN) for image categorization.

     

    Time Series Forecasting

    Time series data, such as stock prices or weather patterns, may be predicted using ARIMA or LSTM.

     

    Recommendation System

    Create a crowdsourced or content-based filtering system for movie recommendations.

     

    Object Detection with YOLO

    Use the YOLO (You Only Look Once) technique to quickly identify objects in pictures or videos.

     

    Healthcare Predictive Modeling

    Using healthcare statistics, develop a prediction model for identifying illnesses or forecasting patient outcomes.

     

    Anomaly Detection in Network Traffic

    Create a network security anomaly detection system utilizing unsupervised learning methods.

    Key Highlights

    Our Instructor

    Learn from professionals who are currently employed and licensed.

    Data Science Training Overview

    Data science is a vibrant, multidisciplinary subject that uses cutting-edge methods and tools to mine huge, intricate databases for insightful information. From data collection, cleansing, and storage through data analysis, modeling, and visualization, it includes a broad variety of tasks. To find patterns, trends, and correlations in data, data scientists use a number of tools and computer languages, such as Python and R. Their analysis may help with data-driven decision-making in many different fields, including as business, healthcare, finance, and other areas. Data science is a crucial component of the artificial intelligence landscape due to the substantial role that machine learning and deep learning algorithms play in predictive modeling and pattern identification.

    Additional Information

    Important Skill Sets Used In Data Science :

    Since data science is an interdisciplinary discipline, success depends on having a wide range of abilities. Some of the key skill sets employed in data science include the following:

    • For the manipulation, analysis, and modeling of data, programming skills in languages like Python and R are essential.
    • Understanding data distributions, drawing conclusions, and creating prediction models all require a solid statistical basis.
    • Building predictive models requires a thorough understanding of supervised and unsupervised learning, machine learning algorithms, and model assessment methods.
    • Being able to use programs like Matplotlib, Seaborn, Tableau, or Power BI to make insightful and instructive data visualizations.
    • Ability to clean, preprocess, and engineer features to prepare data for analysis and modeling.
    • knowledge of huge dataset handling technologies like Hadoop, Spark, and NoSQL databases.
    • Ability to handle and retrieve data from relational databases using SQL.

    What New Data science Frameworks Are There?

    There were a number of well-known frameworks and libraries that were frequently utilized in the area of data science. These included Python libraries for deep learning and neural networks like TensorFlow and PyTorch, for machine learning like Scikit-Learn, for manipulating data like Pandas, and for data visualization like Matplotlib and Seaborn. Furthermore, Apache Spark was a popular choice for processing and analyzing massive data.

    The data science environment is, nevertheless, quite dynamic, and new frameworks and libraries appear to handle shifting data-related difficulties and trends. As new tools and frameworks are continually being developed to satisfy the ever-changing demands of data scientists and analysts, it is advised to consult contemporary resources, blogs, forums, and official documentation within the field to learn about the most recent data science frameworks.

    Necessities of Data science Training:

    For anyone wishing to begin or develop in the area, data science training is essential. The following are some essential requirements for data science training:

    • A strong foundation in arithmetic, statistics, and programming should be the first step in training. Data analysis and modeling need an understanding of these fields' basic principles.
    • Building machine learning models and manipulating data both need proficiency in programming languages like Python and R.
    • Making conclusions, doing hypothesis testing, and comprehending data distributions all need a solid understanding of statistical procedures.
    • For predictive modeling and pattern identification, training in machine learning algorithms, model selection, and assessment is essential.
    • Data preparation for analysis requires expertise in feature engineering, data cleansing, and preprocessing.
    • For the purpose of communicating insights, it's critical to have the capacity to produce illuminating data visualizations utilizing programs like Matplotlib, Seaborn, and visualization libraries.

    Data science training programs give people the opportunity to gain these skills and information, preparing them to work as competent and successful data scientists in a cutthroat industry.

    Data Science Development Trends and Techniques :

    • In data science, AI and ML are still on the cutting edge. For tasks like image recognition, natural language processing, and autonomous systems, GANs (Generative Adversarial Networks), deep learning, reinforcement learning, and other techniques are becoming more popular.
    • A rising problem is ensuring the interpretability and openness of AI models. LIME and SHAP are two explainable AI techniques that aid people in comprehending how sophisticated models make decisions.
    • Through the automation of operations like feature selection, hyperparameter tweaking, and model selection, autoML tools and platforms are streamlining the model creation process.
    • Addressing ethical issues and minimizing bias in AI models are becoming more crucial as AI systems are incorporated into more and more applications.
    • By pushing the limits of NLP, techniques like transformer models, such as BERT and GPT-3, have made it possible for more complex text production and analysis.
    • With the development of models that can be put on edge devices, data science is extending to the edge and making choices in real time without relying on centralized cloud servers.
    • Future Upcoming Technologies Used By Data science Professionals:

      • Quantum computers have the capacity to solve difficult data issues like optimization, cryptography, and simulations at previously unheard-of rates, creating new opportunities for data analysis.
      • Data scientists are focusing more and more on installing AI and machine learning models on edge devices in order to enable real-time processing and decision-making at the source of the data as IoT devices proliferate.
      • With this privacy-preserving method, data may continue to be distributed while model training is still possible. It is especially pertinent in circumstances when data exchange is limited owing to privacy concerns, such as in the healthcare industry.
      • In collaborative data science initiatives, blockchain technology is being investigated to safeguard data sharing and ensure the accuracy of the data.
      • GANs are growing more sophisticated and are used to create realistic data, translate images, and improve data augmentation.
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    Data Science Training Objectives

  • Developing Data Analysis Skills
  • Building Predictive Modeling Expertise
  • Enhancing Data Visualization Abilities
  • Programming Proficiency
  • Data Preprocessing and Cleaning
  • Statistics
  • Programming
  • Machine learning
  • Data visualization
  • Data preprocessing
  • Domain-specific knowledge
  • Organizations may gain from data science training by empowering their staff to utilize data to make data-driven choices, solve issues, and find insights that can result in enhanced business operations, cost savings, and revenue growth.

  • Data scientists
  • Data analysts
  • Machine learning engineers
  • Organizations may gain from data science training by empowering their staff to utilize data to make data-driven choices, address issues, and find insights that might result in enhanced business operations, cost savings, and revenue development.

  • Python
  • R
  • Jupyter notebooks
  • machine learning frameworks
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    Industry Statistics

    Jobs / Month

    248

    Avg. Salary

    ₹ 12,55,200

    Job Roles

    Data Scientist

    ML Engineer

    Data Analyst

    Data Engineer

    Data Science Certification

    Certificate
    GET A SAMPLE CERTIFICATE

    A data science certification is a certificate given to people who have shown their expertise in the subject. Obtaining one often entails finishing a formal training program and passing an exam.

  • Validation of Expertise
  • Career Opportunities
  • Competitive Advantage
  • Skill Improvement
  • Industry Recognition
  • machine learning
  • Data analysis
  • Big data
  • The requirements might change. While some certificates don't require any prior experience, others can call for a specific degree of schooling or programming, math, and statistics background knowledge.

    You may utilize an organized study plan, online lectures, practice examinations, and real-world projects to apply your knowledge as you get ready for a data science certification.

    Yes, a lot of data science certification exams are given online, especially by trustworthy online learning platforms and businesses. Online certification exams are frequently made to be easily accessible and practical for students.

    A data science certification does not usually strictly require previous work experience. Whether or not you have prior experience, data science certificates are usually created to evaluate your knowledge and abilities in data analysis, machine learning, statistics, and related fields. However, depending on the certification program and your professional objectives, the value of real-world experience might change.

    Your unique professional objectives and situation will determine if earning an ACTE data science certification is worth the time and money. A variety of data science certification programs are available from ACTE, and their worth varies based on the program's reputation, its content, your practical experience, and your certification preparation. Data science certificates may strengthen your credentials, enhancing your marketability as a job prospect and perhaps even your earning potential. They also offer access to a professional network in some circumstances and organized learning.

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    The Preferred Partner for 100+ Organizations' Hiring

    Learn from the certified and real time working professionals.

    • Over 100 firms that are looking for top talent for their open positions have come to rely on ACTE as their go-to partner.

    • Businesses have confidence in our ability to match them with the best individuals because of our considerable expertise and proven track record of success.

    • In this section, we'll examine the primary elements influencing this trust and examine how our constant commitment to excellence regularly results in remarkable results for our clients.

    Corporate Clients

    Data Science Course Duration and Fees

    Level Course Duration Fees Structure
    Basic 1 - 1.5 Months ₹7,000 - ₹9,000
    Advanced 1.5 - 2 Months ₹7,000 - ₹10,000

    Job Opportunities in Data Science

    Over 45% of developers say that data science is their preferred field. In the tech sector, Data Science is the most extensively used and sought-after programming language.

    Salary In Data Science
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    Data Science Training FAQ's

    Improve Your Coding Skills: Core Data Science Training for All Levels!

    On your journey to mastering data science, choose the appropriate training program is an essential choice. Start by establishing your professional goals within the data science sector in order to make an educated decision. To find out where you stand, evaluate your present level of knowledge and expertise. Whether you are a novice, an intermediate learner, or someone with expertise, pick the right program level. Check the program's curriculum carefully to make sure it incorporates both theoretical knowledge and hands-on projects and covers the areas that are relevant to your professional goals.

    For a wide spectrum of people interested in working with data, analytics, and machine learning, data science training is advantageous and pertinent.

  • Introduction to Data Science
  • Statistics and Probability
  • Data Collection and Preprocessing
  • Data Exploration and Visualization
  • Data Analysis and Interpretation
  • Machine Learning Fundamentals
  • A typical data science training program's length might vary greatly based on the program's quality, content, and structure, as well as the organization or platform that is offering the training.

  • Career Opportunities
  • Higher Earning Potential
  • In-Demand Skills
  • Problem-Solving Abilities
  • Practical Experience
  • Upon completion, several data science training programs do provide certification. These certificates are an excellent method to demonstrate your expertise to potential employers and develop your data science career.

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    Online educational programs and courses that enable people to acquire data science concepts, tools, and procedures remotely are referred to as online data science training. These training courses are created to give students, professionals, and anybody else interested in gaining data science skills easily accessible and adaptable learning possibilities.

  • Accessibility
  • Flexibility
  • Diverse Course Offerings
  • Cost-Effective
  • Interactive Learning
  • You'll need particular tools and software to participate effectively in your courses for successful online data science training. You'll need a computer with enough processing power, memory, and storage, as well as a dependable high-speed internet connection for streaming videos and gaining access to course materials. A webcam and microphone may be required for video chats, while a contemporary web browser like Google Chrome or Mozilla Firefox is required for accessing online platforms. Furthermore, a good set of headphones can improve the audio quality of video courses.

  • Discussion Forums and Message Boards
  • Office Hours
  • Email Communication
  • Live Webinars and Q&A Sessions
  • Social Media Groups
  • Orientation and Onboarding
  • Course Modules or Units
  • Video Lectures and Readings
  • Hands-On Labs and Assignments
  • Quizzes and Assessments
  • As many programs appeal to people with all levels of skill, including beginners, prior programming knowledge is not often necessary for online data science training. The requirements for completing an online data science course might differ from one to the next.

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    Corporate data science training, sometimes referred to as corporate training in data science, describes the activities and educational programs that businesses give to improve the data science expertise and understanding of its staff members. These training courses, which often concentrate on data analysis, machine learning, and other data-related disciplines, are specifically designed to match the goals and objectives of the firm.

  • Data-Driven Decision-Making
  • Increased Efficiency
  • Improved Productivity
  • Enhanced Problem-Solving
  • Competitive Advantage
  • A thorough and methodical approach is used to personalize corporate data science training to a company's unique needs and goals. It begins with a detailed analysis of the organization's objectives, difficulties, and sector-specific needs. A customized curriculum is created based on this evaluation, highlighting the precise data science subjects and technologies that are most pertinent to the company's industry. To help employees apply what they have learned to practical circumstances, training materials often include case studies, examples particular to the sector, and even the company's own data.

    Corporate data science training is organized in a way that is unique to the requirements of the organization. It starts with a detailed needs analysis to determine the organization's objectives, difficulties, and requirements particular to the sector. The next step is to create a tailored curriculum that outlines data science tools and subjects that are pertinent to the business. To fit employee schedules, the delivery mechanism is chosen, which may include hybrid, online, or on-site training. Whether internal or external, instructors are picked based on their data science proficiency and understanding of the sector.

  • Pre-Training and Post-Training Assessments
  • Surveys and Feedback
  • Performance Metrics
  • Project Outcomes
  • Case Studies
  • Data Analysis and Visualization
  • Statistical Analysis
  • Machine Learning Basics
  • Data Preprocessing
  • Data Manipulation
  • Data Visualization
  • Predictive Modeling
  • Show More