Data Science Certification Training

Data Science Certification Training

Data science and artificial intelligence are two of the most important technologies at the heart of transformation for all sectors and industries. At Securium Solutions, we offer advanced Data Science and AI services that may help you make the most of your data, get the most out of it, and stay ahead in a world that is getting more and more competitive.

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Data Science Course Program Highlights

Fully Live Program

Real-time Interaction

100+ Recorded Video

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Resume Preparation

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Core Components of Data Science and AI Solutions

  • Gathering and getting ready the data

    We collect data from a number of sources, including databases, APIs, logs, social media, and IoT devices, and make sure it is clean, consistent, and useful for analysis.

  • Exploratory Data Analysis (EDA)

    Our professionals employ statistical and visualisation methods to find patterns, outliers, and connections in your data.

  • Engineering Features

    To improve the performance of your ML and AI models, you choose, generate, or change relevant features.

  • Modelling and Machine Learning

    We use methods like regression, classification, clustering, and deep learning to create, train, and test models that are specific to your business needs.

  • Putting it into use and connecting it

    When a model is certified, it is put into your production environment, where it works well with your apps, tools, and infrastructure.

  • Monitoring, Maintenance, and Model Improvement

    Keep an eye on models all the time to make sure they are accurate and work well. We retrain and improve models when fresh data comes in.

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Program Curriculum

Take advantage of this chance to move up in your cyber security career. Sign up for Securium Academy's Cyber Security Master Program Training & Certification and become a protector of the digital world. Our master's program in Cyber Security will make you an expert in this field by educating you about things like malware threats, trojans, cryptography, IAM, security operations, BIA, and more

  • Basics Of Python
  • Data Structures in Python
  • Control Structure And Functions
  • OOP in Python

  • Python NumPy - functions
  • Data Wrangling using Pandas
  • Exploratory Data Analysis Using Matplotlib
  • Exploratory Data Analysis Using Seaborn
  • Data Visualization Using Plots
  • Web Scraping

  • Introduction to Statistics And Understanding the Data
  • Descriptive Statistics, Measures of central tendency, and dispersion
  • Inferential Statistics
  • Probability Distribution, Confidence intervals, and hypothesis testing
  • Sampling Techniques
  • Statistical significance using p-values
  • Regression Analysis And Correlation Analysis
  • Introduction to Bayesian statistics

  • Tableau Overview and its Implementation
  • Power BI-Overview and Its Implementation
  • Google Data Studio and its Implementation
  • Data Analysis using SQL

    1.Introduction to Machine Learning
  • What is ML
  • Why ML
  • Types of ML
  • Main Challenges - Overfitting, Underfitting, Poor
  • Quality data, Irrelevant Features etc
  • What are Hyperparameters
  • How to Select ML model
    • 2.Classification Metrics
  • Accuracy
  • Recall
  • Precision
  • F1 Score
  • Confusion Matrix
  • Classification Report
  • Precision/Recall Tradeoff
  • ROC Curve
  • AOC Curve
  • Binary and Multilabel Classification
  • Feature Engineering and Feature Importance/Selection
    • 3.Classifcation Models
  • Gradient Descent and Stochastic
  • Logistic Regression
  • K Nearest Neighbors
  • Naive Bayes
  • Support Vector Machines
  • Linear Discriminant Analysis
  • Decision Trees
  • Hyperparameter Tuning - GridSearchCV and Randomized Search CV
    • 4.Ensemble Techniques
  • Bagging - Eg: Voting Classifiers
  • Boosting - XG Boost, Adaboost, etc
  • Cross Validation
  • Random Forest Classifier
  • XG Boost Classifier
  • Stacking
  • Hyperparameter Tuning
    • 5.Regression Techniques
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Cost Function and Gradient Descent
  • Performance Metrics - MSE, RMSE, MAE etc
  • Heteroskedasticity, Non Normality and Correlated Errors
  • Hyperparameter Tuning
    • 6.Regression Models
  • Decision Tree Regressor
  • Support Vector Machines
  • K Nearest Neighbors
  • Random Forest
  • Boosting
  • HyperparameterTuning
    • UnsupervisedLearning
  • Introduction to Unsupervised Learning
  • K Means Clustering
  • Hierarchical Clustering
  • Model-Based Clustering
  • DBSCAN
  • Anamoly Detection using Gaussian Mixtures
    • 8.Dimensionality Reduction - Principal Component Analysis 9.RecommendationSystems

    i. Deep Learning using Keras and Tensorflow
  • What is ML
  • Why ML
  • Types of ML
  • Main Challenges - Overfitting, Underfitting, Poor
  • Quality data, Irrelevant Features etc
  • What are Hyperparameters
  • How to Select ML model
    • 2.Implementation using Tensorflow and Keras
  • Building a Neural Network Using Sequential API
  • Building a Neural Network Using Functional API
  • Building a Neural Network using Subclassing API
  • Saving and Restoring a Model
  • Callbacks
    • 3.Training Deep Neural Networks
  • Vanishing/Exploding Gradients
  • Batch Normalization
  • Gradient Clipping
  • Transfer Learning - Using Pretrained Layers
  • Pretraining on AuxiliaryTask
  • Faster Optimizers - RMSprop, AdaGrad, Adam, Nadam, Nesterov Accelerated Gradient
  • Decision Trees
    • 4.Fine Tuning Models
  • How to choose number of hidden layers and number of Neurons
  • Learning Rate, Optimizer, Batch Size Activation Functions
  • L1 and L2 Regularization
  • Dropouts and Batch Normalization
  • Max Norm Regularization

    1.Introduction to Computer Vision
  • The Architecture of Visual Cortex
  • ConvolutionalLayersL
  • Feature Maps
  • Pooling
  • Padding
  • Stacking Multiple Feature Maps
    • 2.Handson Experience - Building an Image Classifier using CNN
    3.Object Detection, Image Segmentation, and Semantic Segmentation
    4.CNN Architectures
    • Learning Predefined Architectures - LeNet, AlexNet, GoogleLeNet, ResNet,VGGNet, Xception, SENet
    • Transfer Learning - Using Pretrained Models from Keras
    • Classification and Localization

    1.Processing Sequences using Recurrent Neural Networks
  • Introduction to Recurrent Neurons and Layers
  • Memory Cells
  • Implementation and Training of Recurrent Neural Networks
  • Time Series using Recurrent Neural Networks
  • Deep RNNs for Time Series
  • Forecasting Several Time Steps Ahead
  • Handling Long Sequences using LSTM and GRU Cells
    • 2.Autoencoder
  • Introduction to Autoencoders
  • Encoder-Decoder Networks
  • Stacked Autoencoders
  • Reconstructing Fashion MNIST Data using Autoencoders
  • Types of Autoencoders - Convolution, Recurrent, Denoising, Sparse and Variational Autoencoders
  • Anamoly Detection Using Autoencoders
    • 3.Generative Adversarial Networks
  • What are GANs? Why GANs?
  • Generator and Discriminator
  • Building a Deep Convolutional GAN on Fashion MNIST Data
    • 4.Reinforcement Learning
  • What is Reinforcement Learning?
  • Learning to Optimize Rewards
  • Policy Search
  • Hands-on Experience Using Open AI Gym
  • The Credit Assignment Problem
  • Q Learning and Deep Q Learning
  • Implementing Deep Q Learning using keras

    1.Introduction to Natural Language Processing
  • Overview of NLP and its Applications
  • Data Preprocessing for NLP
  • Key Components - Tokenization, Stemming and Lemmatization
  • Hands-on Experience - Generating AI Text
  • Sentiment Analysis in NLP using Keras
  • Forecasting Several Time Steps Ahead
    • 2.Neural Machine Translation (NMT)
  • Bidirectional Recurrent Neural Networks
  • Beam Search
  • Sequence to Sequence Model
  • Building a Basic Encoder-Decoder Network for NMT
    • 3.Attention Mechanism
  • Introduction to Attention Mechanisms
  • Visual Attention
  • The Transformer Architecture
  • Fine Tuning NLP Models for NLP Tasks
    • 4.Hands on Experience - Building a Basic Chatbot

    1.Introduction to Natural Language Processing
  • Understanding Professionalism
  • Management Fundamentals- Everything about communication
  • Effective Email Writing
  • Acing Self Introduction and Body Language
  • Resume Fundamentals
  • Mock Interview - I
  • Mock Interview - II
  • Group Discussion
    • 2.Neural Machine Translation (NMT)

    6 Months of Practical & Module Training
  • 1 Month Deep Learning using Keras and TensorFlow Training
  • Management Fundamentals- Everything about communication
  • 1 Month Interview & Resume Building Preparation
  • 3 Months of Advance AI & ML Training

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Skills To Master
Policies for personal safety.
Modeling threats
Things to think about when it comes to risk
Keeping your privacy safe Ownership
Handling requirements
Security of the body
Models for evaluating security
Attacks on networks
How to prove who you are
Architecture for security
Mechanism of authentication
Security architecture

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Cyber Security Training Benefits

Why Choose Us?

Trusted by thousands of learners across the country

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Benefits Of Our Data Science and AI Certification? for Your

Below are some of the reasons that highlights the reasons behind going for Data science and AI certification:

  • AI and data science turn raw data into useful information that helps with strategic planning.
  • You can automate routine operations so that your workers can focus on more valuable work.
  • Use data to guess what will happen in the future, what customers will want, and what hazards might come up to improve results and stay ahead of the competition.
  • Use pattern recognition and recommendation algorithms to provide each customer a unique experience.

Application Areas


  • Business Intelligence and Analytics: It is going to completely change the raw data into reports, dashboards, and forecasts as per your custom needs and requirements.
  • Sales and Marketing Analytics: You will be able to use targeted techniques to learn about customer behaviour and lead to better strategies that can boost conversions.
  • Healthcare Analytics: Find out what dangers patients face, make treatment plans more effective, and make clinical operations more efficient.

Industry Trends

LinkedIn Trends

70,000+ Cyber Security jobs open in the U.S.

Payscale Report

Average salary: $76,559/year for Cyber Security Analysts

Indeed Hiring

Top companies hiring: EY, Tesla & more

Cyber Security Certification

Master’s in Cyber Security is designed by SMEs with 12+ years of experience. Once you complete the course and carry out all the projects successfully, you will receive a master’s degree in Cyber Security and a course completion certificate from Intellipaat and EC-Council.

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Over 20+ Live Interactive Sessions

Gain insights from industry experts on how to develop the skills expected by top recruiters.

Resume & LinkedIn Profile Building

Craft a professional resume and LinkedIn profile that makes you stand out to hiring managers.

Assured Interviews

Guaranteed interviews after project completion through our 500+ hiring partners.

Access to Job Portal

Apply to jobs from 400+ top companies and startups through our dedicated job portal.

  • 6 Months of Practical & Module Training
  • 3 Months Advance AI & ML Training
  • 1 Month Deep Learning using Keras and TensorFlow Training
  • 1 Month Interview & Resume Building Preparation

    • Our Alumni Works At

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    What Our Learners Say

    Hear directly from our students about their experiences at Securium Academy.

    "I have been a student of Securium Academy for a long time and it has helped me become a better cybersecurity expert. They provide the best training you can find and their teachers are very knowledgeable. I always look forward to my classes."

    Avatar
    Partham Yadav
    Cyber Security Student

    "It is one of the best places where one can go and learn about cyber security. He teaches very deeply and makes everything easy. The instructors are very smart and intelligent and make every session fun. Overall I learned a lot from the course I took and I personally highly recommend it."

    Avatar
    Sameer
    CEH V 13 Student

    "Securium Academy is one of the best places to learn. You can visit this website and get knowledge from the best consultant. Along with this, the culture of studies and the method of teaching is also wonderful. I recommend Securium Academy to everyone who wants to learn cybersecurity."

    Avatar
    Peter subraj bor
    Cyber security

    Frequently Asked Questions

    Data science is the process of collecting, preparing, and analyzing data to learn something new. AI uses data science to make decisions or predictions or to automate processes without explicit instructions from a human.

    It is important our experts can develop customized solutions for even small data sets using the right feature engineering and transfer learning, but typically, more data will lead to better AI models.

    Absolutely. Securium Solutions prioritizes data safety through encryption, access control, and implementing appropriate privacy regulations such as GDPR and HIPAA.

    Absolutely. Our team is skilled at incorporating AI and data-driven models into your apps, business tools, and cloud environment all with minimal disruption to existing operations.

    Data scientists use an array of tools and technologies, such as Python and R programming languages, SQL for data querying, Tableau for data visualization, and machine learning libraries like TensorFlow and scikit-learn. Additionally, big data technologies like Hadoop and Spark are employed for processing vast datasets.

    We work with clients in health care, finance, government, IT, manufacturing, logistics, e-commerce, and many other sectors. Each solution is customized to your specific conditions.

    Data Science courses can be instructor-led with live sessions and structured modules, or self-paced with pre-recorded lectures and assignments. Choose the format that aligns with your learning preferences and availability.

    Many Data Science online training incorporate hands-on projects and real-world case studies to provide practical experience and reinforce theoretical concepts. These projects help learners build a strong portfolio for future job opportunities. provided upon course completion?

    Yes, most Data Science training course offer certificates of completion or achievement that can enhance your resume and demonstrate your proficiency in data analysis and related skills.

    Completing a Data Science online course opens doors to various career paths, including Data Analyst, Machine Learning Engineer, Data Scientist, Business Analyst, or roles in Artificial Intelligence development, among others.