We go over all the prerequisites needed to acquire a fantastic job, from the ground up.
Providing complete assistance with the preparation to crack the interviews
We are assisting in finding better and more relevant job openings.
Through guidance ensures the students gain the best out of the course.
Our institute provides easy-to-follow Agentic AI with Data Science training, covering machine learning, data analysis, and statistics. With practical projects and expert guidance, we help you gain real skills to kickstart your career in data science. Join us for hands-on learning and support every step of the way!
In 2005, Syed Mahmood obtained a degree in electronics engineering.He is almost ten years experienced in Excel, Robotics, and Gen AI. He works as a data science trainer.
Mr. Subramanya Swamy H N has been implementing his passion towards teaching from the past 10+ years. He teaches any complex topic in a very simpler manner by giving real time examples such that even naive can easily understands the subject in a crystal clear manner. He is handling Java Full Stack Development, Python Full Stack Development, Devops with AWS, Dotnet Full Stack Development.
• Indexing/ Slicing
• Broadcasting
• Appending/ Inserting on Axis
• Mathematical and Statistical operations
• Sort/ Conditions
• Transpose operations
• Joining/ Splitting
• Linear Algebra
• Data Extraction
• Series/ DataFrame Creations
• Indexing and Slicing
• Conditions/ Grouping/ Imputation
• Append/ Concat/ Merge/ Join
• DateTime Functionalities and Resampling
• Window Functions
• Excel functions
• Customization of Matplotlib/ Seaborn
• Scatterplots/ Barplots/ Histograms/ Density Plots
• 3D plots
• Boxplotting and Outlier Detection
• Visualizing Linear Relationships
• Plotting with Pandas
• Probability
• Basics( Mutually Exclusive/ Joint Probability)
• Conditional Probability
• Dependent/ Independent
• Logs/ Odds
• Bayes Theorem
• Descriptive/ Inferential
• Mean, Median, Mode
• Variance/ Standard Deviation
• Co-variance/ Correlation (Pearson/ Spearman)
• PDF/ PMF/ CDF
• Uniform/ Normal/ Skewed Distributions
• Binomial/ Bernoulli Distribution
• Poisson/ Exponential Distributions
• Central Limit Theorem
• Null/ Alternative Hypothesis
• Z-test/ T-test/ Chi2-test
• p-value
• F-test/ Anova
• Scipy.Stats/ Statsmodels
• Difference Between AI, ML and DL
• Applications of Machine Learning
• Categorization of Machine Learning
• Supervised / Unsupervised / Semi Supervised
• Parametric vs Non Parametric
• Geometric/ Rule Based/ Gaussian
• Flow Operation (Pipelining)
• Sklearn Usage
• Null Values Imputation
• Outlier Detection
• Encoding
• Label Encoder
• Ordinal Encoding
• One Hot Encoding
• Scaling
• Binarizer
• MinMaxScaling
• Normalizer (L1 and L2)
• StandardScaler
• Imbalance Dataset
• Univariate/ Bivariate/ Multivariate Analysis
• Filter Methods
• Wrapper Methods
• Embedding
• Assumptions
• Introduction to Linear Regression
• Understanding the real meaning of Linear Regression
• Multiple Linear Regression
• Cost Function (Sum of Square Error)
• Loss Function
• Derivative of Loss
• Gradient Descent for Multiple Features
• Introduction to Polynomial Regression
• When to use Polynomial regression
• Evaluation based on RMSE/ R2
• Problems with Large Features
• Why penalty is inducted
• Difference between L1 and L2
• Cost Function
• Introduction to KNN algorithm
• KNN Classifier vs Regressor
• How to select the best K
• Logistic regression vs Linear Regression
• Log Odds / Logit / Sigmoid Function
• Optimization and Log Loss
• Maximum Likelihood Estimation
• Margin of SVM’s
• SVM optimization
• SVM for Data which is not linear separable
• Kernel Trick
• SVM Parameter Tuning
• Hinge Loss
• Introduction to Decision tree
• Decision Tree Classification / Regression
• Types of Decision Tree techniques (ID3 / CART)
• Pruning
• Conditional Probability and Bayes Theorem
• Naive Bayes
• Burnoulli / Multinomial and Gaussian Implementation
• Holdout Validation
• K-fold cross Validation
• Stratified Kfold
• Cross_val_score
• GridSearchCV
• RandomizedSearchCV
• MSE/ MAE
• R2/Adjusted R2
• Accuracy measurement
• Confusion Matrix
• Precision/ Sensitivity/ Specificity/ F1 Score
• AUC/ ROC
• AIC and BIC
• Voting/ Averaging
• Bagging / Boosting / Stacking
• Random Forest
• AdaBoost
• Gradient Boosting
• XGBoost
K Means
• Applications of Clustering
• Understanding Euclidean Distance
• Basics of Clustering
• Elbow Method and Silhouette score
Hierarchical Clustering
• Agglomerative
• Divisive
DBSCAN
• Reachability
• Connectivity
• epsilon and r
GMM
• Gaussian surface
• Relation with EM
• Difference between GM and other Clusters
• What is PCA?
• Understanding Matrix Transformations
• Eigen Values and Eigen Vectors
• tSNE and Umap
• Lag Values
• AutoRegression/ AutoCorrelation
• Stationarity
• Dicky Fuller Test
• Time Series Decomposition
• Modelling and Forecasting
• ARIMA/ SARIMA
• Support, Confidence, Lift
• Jacard Matrix
• Cosine Similarity
• Perceptron and relate it with Logistic Regression
• Multiple layer Neural network
• Similiarities and Differences with Baisc ML
• Forward Propogation
• Back Propagation Algorithm
• Vanishing Gradient and Exploding Gradient
• Non Linearity
• Sigmoid / Tanh Function
• Relu /Leaky Relu /Gelu
• Softmax Function
• Gradient Descent
• Stochastic Gradient Descent
• Momentum
• AdaGrad
• RMSProp
• NAG
• Adam/ Nadam
• Tensors
• Session, Placeholders and Variables
• Hands on with Tensorflow
• Sequential vs Functional
• Model Creation
• Difference between Tensorflow and Pytorch
• Autograd
• Graphs
• Data Loaders
• Feed Forward Networks
• Fully Connected Networks
• Recurrent Neural Networks
• Convolutional Networks
• Convolution/ Filters/ Pooling
• Back Propogation in CNN
• Image Recognition vs Object Localization
• Types Of CNN
• FastRCNN, YOLO
• LeNet/ Alexnet
• VGG 16/ 19
• ResNet
• MobileNet
• ImageNet
• Need of Transfer Learning
• Freezing of Layers
• Reusing of Structure
• Encoder vs Decoder
• Difference with PCA
• KL Divergence
• Variable Auto Encoders
• Generators
• Discriminator
• Structure
• Word Embedding
• Frequency vs Prediction based embedding
• Count Vector/ TFIDF/ Co Occurrence
• Bag of Words / Skip Gram
• Word2Vec /GloVe
• Classical RNN
• LSTM/GRU
• Vanishing Gradient
• Exploding Gradient
• Bidirectional RNN
• Encoder and Decoder
• Encoder only Transformers
• Decoder only Transformers
• Prompt Engineering
• Chain of Thought (CoT)
• Components of Langchain
• Open AI and Hugging Face
• How to query database
• Multi Instruction Fine Tuning
• Parameter Efficient (PEFT)
• LoRA
• Quantization
• QLoRA
• Difference between Retrieval and Augmentation
• Limitations of RAG
• ReAct
• Knowledge Graphs
• Vector Database
• PAL model
• MDP
• Value Functions
• State Values vs Action Value (Q)
• Exploitation vs Exploration
• TD Learning (Q Learning vs Sarsa)
• Hands on RL
• Tensorflow Agents Library
• OpenAI Gym environment
• Deep Q Networks
• Policy Gradient Methods
• Actor-Critic Architectures
• Gradient Based Techniques
• Proximal Policy Optimization (PPO)
• RLHF
Learn data science basics like machine learning and data analysis.
Work on real projects to practice what you learn.
Get help and guidance from experienced instructors.
Build skills that prepare you for a job in data science.
The Data Science course by Global Quest Technologies is especially designed to acquaint the individuals from the very root of introduction to Data Science to the expert-level practical applications of it. The students get complete guidance not only for clarifying course-related doubts but also regarding the preparation for placements.
Data Science is a 6-month long course that would teach you the basics of Data Science, AI, ML and more. It will also prepare you with appropriate soft skills that would be beneficial for the placements and interviews.
For the complete curriculum and the placement assistance through the Data Science course offered by GQT, it would cost you INR 50,000.
Students looking forward to building a career as Professional Data Scientists can enroll themselves in the GQT’s Data Science course. The graduation and post graduation students looking forward to upskill their profile to land better jobs are also welcome to apply.
Yes, GQT provide a complete placement assistance along with the necessary skill development for the students so that they can land on better jobs. We also prepare the students with rigorous mock interviews and tests to prepare them for the corporate world.
