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Data Scientist Roadmap
4.9 (24922 Reviews)Data Scientists are professionals equipped with a blend of statistical, mathematical, programming, and domain-specific expertise. They play a pivotal role in extracting insights and valuable information from vast and complex datasets to guide strategic decisions and solve intricate problems across various industries.
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Upcoming Batch Details.
Duration
- (Mon - Sat) 5 Months
- (Mon - Sat) 5 Months
- (Mon - Sat) 5 Months
- (Mon - Sat) 5 Months
- (Mon - Sat) 5 Months
Timings
- 9:00 AM to 10:00 AM
- 10:00 AM to 11:00 AM
- 4:00 PM to 5:00 PM
- 5:00 PM to 6:00 PM
- 6:00 PM to 7:00 PM
Overview of Our Data Scientist Course
Their primary responsibility revolves around analyzing, interpreting, and deriving actionable insights from data. This involves employing statistical models, machine learning algorithms, and data mining techniques to uncover patterns, trends, and correlations within the data. Data Scientists use programming languages like Python, R, or SQL, and tools such as TensorFlow or scikit-learn to manipulate and analyze data effectively.
Data cleaning and preprocessing constitute a significant portion of their work, involving handling missing values, outliers, and ensuring data quality to make it suitable for analysis. They explore data using exploratory data analysis (EDA) techniques to understand its characteristics before applying advanced modeling techniques.
Moreover, Data Scientists design and build predictive models to forecast future trends or outcomes, classification algorithms for categorization, recommendation systems, and clustering techniques for segmentation purposes. They evaluate the performance of these models using metrics and iterate upon them to improve accuracy and reliability.
Curriculum of Data Scientist Course
Well-structured & comprehensive curriculum designed according to latest trends and industry standards!
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Mathematics and Statistics:
- Linear algebra, calculus, probability, and statistics.
- Courses from platforms like Khan Academy, Coursera, or edX.
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Programming Skills:
- Python or R for data analysis.
- Interactive platforms like Codecademy or DataCamp.
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Data Manipulation and Analysis:
- Pandas library in Python or data.table in R.
- Work on small datasets to practice data cleaning and exploration.
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Data Visualization Libraries:
- Matplotlib, Seaborn, ggplot2.
- Create compelling visualizations to communicate insights.
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Visualization Tools:
- Tableau, Power BI.
- Learn to create interactive dashboards.
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Cleaning Techniques:
- Handling missing data, outliers, and duplicates.
- Feature engineering for creating relevant features.
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Data Wrangling Tools:
- Apply techniques using Pandas or dplyr.
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Supervised Learning:
- Understand regression and classification.
- Implement algorithms like linear regression, logistic regression.
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Unsupervised Learning:
- Clustering and dimensionality reduction.
- Practice with k-means, hierarchical clustering, PCA.
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Ensemble Learning:
- Random Forest, Gradient Boosting.
- Ensemble techniques for better model performance.
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Cross-Validation:
- K-fold cross-validation.
- Ensure models generalize well to unseen data.
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Metrics:
- Understand evaluation metrics like accuracy, precision, recall, F1-score.
- ROC curves for binary classification.
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Deep Learning Basics:
- Neural networks, activation functions.
- TensorFlow or PyTorch for implementation.
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Reinforcement Learning (Optional):
- Basics of reinforcement learning.
- Apply algorithms like Q-learning or Deep Q Network.
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Text Preprocessing:
- Tokenization, stemming, lemmatization.
- Use NLTK or spaCy.
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NLP Libraries:
- TextBlob, Gensim.
- Implement sentiment analysis, text summarization.
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Hadoop Ecosystem:
- HDFS, MapReduce, Hive.
- Understand big data processing.
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Spark:
- Apache Spark for distributed computing.
- Implement data processing tasks using PySpark.
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Model Deployment Platforms:
- Docker, Kubernetes.
- Deploy models using Flask, FastAPI, or TensorFlow Serving.
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Cloud Platforms:
- AWS, Azure, GCP.
- Deploy models on cloud platforms.
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Git and GitHub:
- Learn version control for collaboration.
- Contribute to open-source projects.
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SQL:
- Querying databases for data extraction.
- Use platforms like Mode Analytics or SQLZoo.
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NoSQL Databases (Optional):
- MongoDB, Cassandra.
- Understand non-relational databases.
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Designing Experiments:
- A/B testing principles.
- Evaluate statistical significance.
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Intensive Classroom Training
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Hands-on 12 Live Projects
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Job Assistance
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