Today, data science is one of the most in-demand and fastest-growing fields of computer science, and data scientists are some of the most highly paid-engineers in the tech field. Here are some facts about data scientists:
- The demand for data scientists is projected to grow by 35% over the next decade.
- Data scientists earn between $95,000 and over $230,000 a year.
- Machine learning is an essential skill, with 77% of AI-related job postings requiring ML expertise.
Some freshers or non-technical individuals want to become data scientists. This field primarily has three primary career paths: Data Analyst, AI/ML Engineer, and Data Scientist.
1. Data Analysis
If you have a technical background, you have some advantages because AI/ML work involves mathematics, visualization, and some technical knowledge. However, even if you are not from a technical background or have strong math and statistics knowledge, you can still become a data scientist.
The most important thing is "DATA." Technical knowledge is okay, but domain knowledge and data understanding work equally. Data science revolves around working with data. If you have clean data, you don’t need to spend time purifying it, checking for NaN or missing fields, or correcting wrong data. On the other hand, if you have leverage data, you need to check data types, clean it, fill in missing values, and perform many other tasks.
First Step to Becoming a Data Scientist
We need to learn Python programming basics, visualization tools like Tableau or Power BI, and, most importantly, statistics. Apart from this,
1.1. Data
Data is a collection of information and statistics, and it can take various forms, such as numbers, text, sound, images, or any other format.
There are four types of data in the data science
- Nominal
- ordinal
- Interval
- ratio
Here is a getting started book on Python and data: Download Book: Python Overview
2. Second Step - Machine Learning and Advanced Machine Learning
In machine learning, you will encounter more than 10 algorithms and various technical steps for data cleaning, preprocessing, and imputation. The machine learning is essential because all algorithms and problem solutions apply to the data.
Her is a book on Machine Learning/: Download Book: Machine Learning for Future Engineers.
Some essential steps of data processing and EDA are as follows:
Introduction
Problem Introduction If I chose cancer data, we must introduce the data.
Problem Statement
The first step in any data clearly defines the problem you are trying to solve.
Import Libraries
Import the necessary Python libraries
- Pandas: For data manipulation and analysis.
- NumPy: For numerical computations.
- Matplotlib/Seaborn: For data visualization.
- Scikit-learn: For machine learning algorithms and model evaluation.
Here is a book on these libraries: Download Book: Python Libraries for Machine Learning
Data Acquisition
Data acquisition involves gathering the data from various sources.
Example. Loading data from a CSV file
data = pd.read_csv("xyz.csv")
Data Pre-Profiling
- Data Overview
- Missing Values
- Duplicates
- Outliers
Data Preprocessing
- Handling Missing Values
- Removing Duplicates
- Outlier Treatment
- Feature Engineering
Data Post-Profiling
After preprocessing, it's important to re-arrange the data to ensure that all issues have been Resolved.
The next step is to split the data into features (X) and target (Y).
- X = data.drop('HeartDisease', axis=1)
- y = data['HeartDisease']
Train-Test Split
To evaluate the performance of a machine learning model, the data is split into training and testing sets.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Continuous and Categorical Split
Identifying continuous and categorical columns.
continuous_cols = X.select_dtypes(include=["float64", "int64"]).columns
categorical_cols = X.select_dtypes(include=["object", "category"]).columns
Encoding
One-Hot Encoding for categorical columns.
preprocessor = ColumnTransformer(
transformers=[
("num", StandardScaler(), continuous_cols),
("cat", OneHotEncoder(), categorical_cols),
]
)
X_train = preprocessor.fit_transform(X_train)
X_test = preprocessor.transform(X_test)
Scaling
Scaling is important for algorithms that are sensitive input features, such as K-Nearest Neighbors (KNN) and Support Vector Machines (SVM).
Scaling continuous features scaler = StandardScaler()
X_train[continuous_cols] = scaler.fit_transform(X_train[continuous_cols])
X_test[continuous_cols] = scaler.transform(X_test[continuous_cols])
Concatenating
After preprocessing, the continuous and categorical features are concatenated back together to form the final dataset ready for modeling.
Concatenating continuous and categorical features
X_train = np.concatenate([X_train[continuous_cols], X_train[categorical_cols]], axis=1)
X_test = np.concatenate([X_test[continuous_cols], X_test[categorical_cols]], axis=1)
EDA-2 Steps (Assumption Check Strategy)
Exploratory Data Analysis (EDA) is an iterative process. After preprocessing, it's important to revisit EDA to check assumptions and validate that the data is ready for modeling.
Example. Checking the correlation between features
corr_matrix = data.corr()
sns.heatmap(corr_matrix, annot=True, cmap="coolwarm")
plt.show()
Applying Machine Learning Algorithms
With the data clean and preprocessed, various machine learning algorithms can be applied to build predictive models some also like
- K-Nearest Neighbors (KNN)
- Linear Regression
- Logistic Regression
- Random Forest
- Support Vector Machines (SVM)
Example. Applying a Random Forest Classifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
Model Evaluation
Evaluating the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")
conf_matrix = confusion_matrix(y_test, y_pred)
sns.heatmap(conf_matrix, annot=True, fmt="d", cmap="Blues")
plt.show()
Conclusion
Now our models are done, if the model working fine it's okay but if the model works fine we will apply all algorithms or evaluation models again and again.
3. Knowledge of ML Algorithms - The Final Step to Becoming a Data Scientist or AI/ML Engineer
After mastering machine learning, you need to gain knowledge of neural networks, computer vision, and advanced Python.
If you work with neural networks or computer vision models, you will need a large amount of data for processing. A machine learning model typically works with lakhs (hundreds of thousands) of data fields, while neural networks require 10x more data than traditional ML models. These algorithms are implemented using advanced libraries like PySpark.
After this, you can become a data scientist. Additionally, some domain knowledge of cloud computing is essential for roles like Data Engineer. Data engineers work with large-scale data and use ETL processes, PySpark, and Hadoop for data processing.